![Eyes Open and Eyes Close Activity
Recognition Using EEG Signals
Barjinder Kaur1(✉)
, Dinesh Singh1
, and Partha Pratim Roy2
1
DCRUST, Sonepat, India
kaur.barjinder@gmail.com, dinesh.madhav@gmail.com
2
IIT Roorkee, Roorkee, India
proy.fcs@iitr.ac.in
Abstract. So far Electroencephalography (EEG) has been analyzed by the re-
search community for interaction with the computers. Studies regarding EEG
signals has gained attention in the recent past as it gives an alternate way of com-
munication for the persons suffering from partially or fully paralytic disability.
Every second different activities are performed by millions of neurons. Decoding
and detecting such complex activity of the brain while analyzing the EEG signals
is a challenging task. In this paper, we have proposed an activity recognition
system using EEG signals. The two activities, namely, eyes open (EO) and eyes
close (EC) have been considered in this work. The recorded signals are then
decomposed using Discrete Wavelet Transform (DWT) to analyze the impact of
both the activities. The recognition of activities has been performed using Support
Vector Machine (SVM) classifier. For experimentation, a publicly available
dataset i.e. PhysioNet consisting data of 109 users while performing one minute
EO and EC activity has been used. A notable activity recognition rate of 86.08%
has been recorded using gamma band feature. The paper further proposes that the
system can be used as a reference to detect different types of activities performed
at different instance of time and for rehabilitation purposes also.
Keywords: Electroencephalography (EEG) · Eyes open (EO) · Eyes close (EC)
Discrete Wavelet Transform (DWT) · Support Vector Machine (SVM)
1 Introduction
Brain Computer Interface (BCI) is a mechanism of acquiring the neuro signals. These
acquired signals store mirage of information that has been proved helpful in diagnosing
various diseases, and providing help for motor-disabled people. Various methods like
Electroencephalogram (EEG), Magnetoencephalogram (MEG), functional Magnetic
Resonance Imaging (fMRI), Positron Emission Tomography (PET), etc. are available
to capture the brain functionality. However, taking into account the cost and easy port‐
able aspect, EEG has been widely used by researchers [5, 6, 14] as it provides a non-
invasive monitoring method to analyze the brain recordings, and is frequently used to
implement different BCI systems.
The brain oscillations are usually measured between 0.5–40 Hz frequency range.
© Springer Nature Singapore Pte Ltd. 2018
T. N. Nagabhushan et al. (Eds.): CCIP 2017, CCIS 801, pp. 3–9, 2018.
https://doi.org/10.1007/978-981-10-9059-2_1](https://image.slidesharecdn.com/35553-250719013157-0b1129e1/85/Cognitive-Computing-and-Information-Processing-T-N-Nagabhushan-26-320.jpg)
![Thus, based on these measurements EEG signals are segregated into five rhythms,
namely, delta (0.5–<4 Hz), theta (4–7 Hz), alpha (8–15 Hz), beta (16–32 Hz) and gamma
(32 Hz above) that are omnipresent in different parts of the brain [5]. EEG helps in
detecting the various abnormalities of the brain like sleeping disorders, emotional
variance, seizures detection, and in the recent past researchers have been widely using
EEG signals for proposing user identification systems [5, 6], Neuromarketing [14],
rating prediction systems [4] and envisioned speech recognition [7]. Although normal
EEGpatterns are easilyrecognizable, but to evaluate the reason of change in EEG signals
is a cumbersome task. The human brain consists of about 86 million neurons which helps
in controlling all the conscious and unconscious daily decisions [3]. Recognizing and
detecting the activity that could have lead to abnormal behavior of the neuro signals has
made the researchers to analyze the activity happened during the course of action.
Saghafi et al. [11] used power spectrum, variance, maximum, and range features to
discriminate between the eye state change. The authors have used Logistic Regression
and Artificial Neural Network (ANN) classifier to evaluate the system performance
whereas an auto regressive power feature has been used to discriminate between eyes
close and eyes open state. High frequency power has been reported in frontal, parietal,
and occipital areas in eyes close state.
Öner et al. [8] investigated the moments of the eyes using one-channel. The
authors focused on the ocular movements of the eye. The features were selected
using analysis of variance (ANOVA) function and a linear fit model has been used
to report the higher presence of delta rhythm while performing the task. However,
their study was conducted with EEG data of 2 subjects. In [13], the authors have
analyzed left and right hemisphere of the brain using EEG signals recorded in eyes
open state. Occipital lobe and left hemisphere were found more active by analyzing
alpha band waves. Qidwai et al. [10] analyzed the data of six subjects to discrimi‐
nate the two motor activities EO and EC. Different angle features of the eye states
were used for feature extraction and fed directly into a Multilayer Perception (MLP)
based Neural Network classifier for recognition purpose. The authors in [2] have
proposed a methodology to detect the left and right fist motor movement activity
using EEG signals of 6 subjects only. The signals were filtered with a notch filter and
were further preprocessed using Automatic Artifact Removal (AAR) technique. They
have used DWT based signal decomposition technique and three different features,
namely, power, mean and energy were extracted for classification purpose. Simi‐
larly, the classification of imagined left and right fists movement was proposed in [1]
using EEG signals of 20 users. Root Mean Square (RMS) and Mean Absolute Value
(MAV) features were extracted and modeled using SVM and ANN classifiers where
accuracies of 84.5% and 82.1% were recorded from 3 channels, respectively.
Accurate recognition of the performed activity by analyzing EEG signals not only
proves helpful in healthcare but also contributes towards self-evaluation of changes in
daily health parameters. Therefore, in this paper, we analyzed the neuro signals for
activity recognition. For this, two activities, namely, eyes open (EO) and eyes close (EC)
are considered. Figure 1 depicts the flow diagram of the proposed approach, where brain
signals are recorded in one of the two states i.e. EO and EC. The recorded signals are
then preprocessed and two features, namely, Standard Deviation (STD) and RMS are
4 B. Kaur et al.](https://image.slidesharecdn.com/35553-250719013157-0b1129e1/85/Cognitive-Computing-and-Information-Processing-T-N-Nagabhushan-27-320.jpg)
![extracted. Finally, the recognition of activities is performed using Support Vector
Machine (SVM) classifier.
Fig. 1. Framework for eyes open and eyes close activity recognition using EEG Signals.
Rest of the paper is organized as follows. The description of signals preprocessing
and feature extraction techniques are presented in Sect. 2. Section 3 presents the exper‐
imental results. Finally, we conclude the paper in Sect. 4 by highlighting some future
possibilities of the present work.
2 Signal Preprocessing and Features
Here, we present the details of the preprocessing techniques used to filter the raw signal
data and the features extracted before classification of the activities.
Preprocessing. It has been analyzed that while recording the data, the EEG signals are
always contaminated with unwanted noise from the surroundings, hair, ocular move‐
ments etc. To get the enhanced performance of the system these artifacts need to be
removed. Bio-signals being non-stationary in nature tends to get distorted due to sensi‐
tiveness of the device. Also, it has been researched that the human brain activities are
limited upto 40 Hz, where higher value signifies noise. Therefore, to remove such noise
and to restrict the frequency till 40 Hz, a low pass filter is used. For this, we have used
a Butterworth low pass filter of 5th
order to preprocess [9] the EEG signals. The filter
can be defined using (1).
H(z) =
q(1) + q(2)z−1
+ … + q(n + 1)z−n
1 + p(2)z−1 + … + q(n + 1)z−1
(1)
where n denotes the filter order with normalized cutoff frequency Wn. It returns the filter
coefficients of length n + 1 as row vectors q and p, along with the coefficients z in
descending powers.
Wavelet Analysis and Statistical Features. As EEG signals are non-stationary, there-
fore, DWT provides a flexible way to analyze the signals in time-frequency domain [5,
14]. It is an analysis technique that is based on sub-band decomposition coding pro-
viding easy implementation and also helps in reducing the computation time. DWT is
Eyes Open and Eyes Close Activity Recognition Using EEG Signals 5](https://image.slidesharecdn.com/35553-250719013157-0b1129e1/85/Cognitive-Computing-and-Information-Processing-T-N-Nagabhushan-28-320.jpg)
![widely used in applications including emotion detection, security, seizure detection and
gaming, etc. In this work, we have used Daubechies-4 (DB-4) wavelet to decompose
the EEG signals into five sub-bands, namely, gamma, beta, alpha, theta and delta. Next,
two statistical features i.e. STD and RMS have been extracted from the sub-bands. More
details about the DWT and features can be found in [5].
2.1 Activity Recognition Using SVM
SVM has been widely used by researchers for the classification of EEG signals. It sepa‐
rates the samples into two different classes using the hyperplane [5]. It transforms the
original feature space into a high dimensional space with maximum margin. Different
kernel functions are used to perform the mapping by performing calculations in the data
space and returns it to feature space. For given training data, (x1, y1), (x2, y2), (xn, yn), the
decision function f (x) is defined in (2).
f(x) = sgn{(w ⋅ x) + b} = sgn
(
∑
i=1
yiaik < xi, x > +b
)
(2)
Here (w ⋅ x) represents the dot product between weight vector w and x data with
binary class. The parameter b is defined as a scalar that is often referred to as the bias.
The term αi denotes the embedding coefficients and k <xi, x> is the kernel represented
by the dot product i.e. <., .>. The experimental results are classified by varying kernel
functions i.e. linear and Radial Basis Function (RBF).
3 Results
Here, the details of the dataset and the results of activity recognition are presented. The
computation of the results has been performed using 2-fold cross validation. The details
are as follows.
3.1 DataSet Description
For activity recognition, a freely online available EEG-based motor movement and
imaginary dataset provided by PhysioNet BCI [12] has been used. EEG signals were
recorded from all 64 channels for 1 min. Two baseline tasks, eyes open (EO) and eyes
close (EC) resting state have been used to collect the data from 109 users and are
considered in this work. In order to detect each activity accurately, the EEG data has
been segmented into 10 s. Thus, a total of 1308 EEG files (i.e. 654 for EC and 654 for
EO) have been created for analysis.
3.2 Activity Recognition Results Using SVM
The SVM classifier has been trained for recognizing the activity performed by the users
using the extracted features. The accuracies have been computed for all 5 sub-bands.
6 B. Kaur et al.](https://image.slidesharecdn.com/35553-250719013157-0b1129e1/85/Cognitive-Computing-and-Information-Processing-T-N-Nagabhushan-29-320.jpg)
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Cognitive Computing and Information Processing T.N. Nagabhushan
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- 4. 123 T. N. Nagabhushan V. N. Manjunath Aradhya Prabhudev Jagadeesh Seema Shukla Chayadevi M. L. (Eds.) Third International Conference, CCIP 2017 Bengaluru, India, December 15–16, 2017 Revised Selected Papers Cognitive Computing and Information Processing Communications in Computer and Information Science 801
- 5. Communications in Computer and Information Science 801 Commenced Publication in 2007 Founding and Former Series Editors: Alfredo Cuzzocrea, Xiaoyong Du, Orhun Kara, Ting Liu, Dominik Ślęzak, and Xiaokang Yang Editorial Board Simone Diniz Junqueira Barbosa Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil Phoebe Chen La Trobe University, Melbourne, Australia Joaquim Filipe Polytechnic Institute of Setúbal, Setúbal, Portugal Igor Kotenko St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia Krishna M. Sivalingam Indian Institute of Technology Madras, Chennai, India Takashi Washio Osaka University, Osaka, Japan Junsong Yuan Nanyang Technological University, Singapore, Singapore Lizhu Zhou Tsinghua University, Beijing, China
- 6. More information about this series at http://www.springer.com/series/7899
- 7. T. N. Nagabhushan • V. N. Manjunath Aradhya Prabhudev Jagadeesh • Seema Shukla Chayadevi M. L. (Eds.) Cognitive Computing and Information Processing Third International Conference, CCIP 2017 Bengaluru, India, December 15–16, 2017 Revised Selected Papers 123
- 8. Editors T. N. Nagabhushan Sri Jayachamarajendra College of Engineering Mysuru, Karnataka India V. N. Manjunath Aradhya Sri Jayachamarajendra College of Engineering Mysuru, Karnataka India Prabhudev Jagadeesh JSS Academy of Technical Education Bengaluru, Karnataka India Seema Shukla JSS Academy of Technical Education Noida, Uttar Pradesh India Chayadevi M. L. JSS Academy of Technical Education Bengaluru, Karnataka India ISSN 1865-0929 ISSN 1865-0937 (electronic) Communications in Computer and Information Science ISBN 978-981-10-9058-5 ISBN 978-981-10-9059-2 (eBook) https://doi.org/10.1007/978-981-10-9059-2 Library of Congress Control Number: 2018937399 © Springer Nature Singapore Pte Ltd. 2018 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. part of Springer Nature The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
- 9. Preface The editorial team is honored to announce the publication of the conference pro- ceedings of the Third International Conference on Cognitive Computing and Infor- mation Processing (CCIP 2017). The conference, which was jointly organized by the Departments of Computer Science and Information Science and Engineering of JSS Academy of Technical Education, Bengaluru, was the third in the series. The focus of the conference was to understand the recent advancements in the cognitive era of computing systems that emulate the capability of human mind. The conference provided a forum for researchers and practitioners to present and discuss new research results, practical applications, and also promote collaborative research activities in cognitive computing and information processing and related field. There were 130 papers submitted to CCIP-2017 (General track and four special sessions, namely, Cognitive Computing in Video Analytics, Cognitive Computing for Applications on Smart City, Cognitive Computing in Medical Information Processing, and Women in Information Processing) and they all underwent a rigorous review process. Upholding the quality requirements, around 33% of the papers received were selected for presentation. The conference also comprised a pre-conference tutorial from Dr. Manjunath S., Samsung Electro Mechanics, and Dr. Punitha, IBM and, as well as keynote talks from Prof. N. Sundararajan, NTU, Singapore, Prof. Michael Carl, Copenhagen Business School, Denmark, Dr. M. Pratama, NTU, Singapore, Prof. S. R. M. Prasanna, IIT Guwahati, Prof. Savitha Ramasamy, ASTAR, Singapore, and Dr. Vigneswaran, ASTAR Singapore. We would like to gratefully acknowledge the support received from J.S.S. Mahavidyapeetha in organizing this conference. We are grateful to all the researchers, reviewers, speakers, and the organizing team for helping us attain the objectives of the conference. We are sure that this conference has opened up a new platform for the researchers to share their ideas for continuous improvement. We hope that the conference proceedings will be inspiring to readers to pursue further research in the area of cognitive computing and information processing. March 2018 T. N. Nagabhushan V. N. Manjunath Aradhya Prabhudev Jagadeesh Seema Shukla Chayadevi M. L.
- 10. Organization Chief Patron His Holiness Jagadguru Sri Shivarathri Deshikendra Mahaswamigalavaru Veerasimhasana Math, Sutturu Srikshethra, President, JSSMVP, Mysuru Patrons C. G. Betsurmath JSSMVP, Mysuru M. H. Dhananjaya JSSMVP, Mysuru C. Ranganathaiah JSSMVP, Mysuru B. G. Sangameshwara JSSSTU, Mysuru Karisiddappa VTU, Belagavi B. R. Umakanth JSSMVP, Mysuru Co-patrons Mrityunjaya V. Latte JSSATE, Bengaluru T. N. Nagabhushan SJCE, Mysuru General Chairs Prabhudev Jagadeesh JSSATE, Bengaluru Chayadevi M. L. JSSATE, Bengaluru Advisory Committee H. N. Jagannatha Reddy VTU, Belagavi, India Satish Annigeri VTU, Belagavi, India D. K. Subramanian IISc, Bengaluru, India S. S. Iyengar Florida International University, USA Ajith Abraham MIR Labs (Global Operations), USA Buyya Rajkumar University of Melbourne, Australia Venkatesh Babu R. IISc, Bengaluru, India Erik Cambria SCE, NTU, Singapore K. Subramanian NTU, Singapore Haijun Rong Xi’an Jiaotong University, China V. Mani IISc, Bengaluru, India R. V. Babu IISc, Bengaluru, India F. Bremond Inria-Sophia Antipolis, France
- 11. P. B. Sujit IIIT, Delhi, India S. Vasaily Catholic University, Seoul, South Korea G. S. Babu SimTech, Singapore S. Jagannathan MUST, USA Guang-Bin Huang NTU, Singapore B. Tripathi HB Technology, India Ravindran IIT, Chennai, India Shigeyoshi Tsutsui Osaka Prefecture University, Japan Kirill Krinkin Russia Pradeep Kumar Amphisoft Technologies, Coimbatore, India Harsha Motorola, Bengaluru, India Dinesh M. S. Philips, Bengaluru, India S. R. Mahadeva Prasanna IIT, Guwahati, India Suryakanth V. Gangashetty IIIT, Hyderabad, India S. Manjunath Samsung, India Steering Committee Chair K. Chidananda Gowda Kuvempu University, Shivamogga, India Steering Committee N. Sundararajan EEE, NTU, Singapore S. Suresh SCE, NTU, Singapore H. J. Kim Korea University, Seoul, South Korea Prabhu Shankar UCDavis, USA Oleg Medvedev Moscow State University, Russia Hanseok Ko Korea University, Seoul, Korea S. N. Omkar IISc, Bengaluru, India Jayadeva IIT, Delhi, India P. Nagabhushan IIIT, Allahabad, India Guru D. S. University of Mysore, India S. K. Padma SJCE, Mysuru, India Nagabhushana JSSATE, Bengaluru, India Swamy D. R. JSSATE, Bengaluru, India Organizing Chairs N. C. Naveen JSSATE, Bengaluru Dayananda P. JSSATE, Bengaluru Technical Program Chair V. N. Manjunath Aradhya SJCE, Mysuru VIII Organization
- 12. Technical Program Co-chairs D. V. Ashoka JSSATE, Bengaluru P. B. Mallikarjuna JSSATE, Bengaluru Special Session Chairs B. S. Mahanand SJCE, Mysuru Dinesh R. Samsung, India Vinita Khemchandani JSSATE, Noida Anita Sahoo JSSATE, Noida M. P. Pushpalatha SJCE, Mysuru M. A. Anusuya SJCE, Mysuru Publication Chairs Malini M. Patil JSSATE, Bengaluru Sneha Y. S. JSSATE, Bengaluru Seema Shukla JSSATE, Noida Tutorial Chair Nagasundara K. B. JSSATE, Bengaluru Finance Chair Snehalatha N. JSSATE, Bengaluru Publicity Chairs Sharana Basavana Gowda JSSATE, Bengaluru Abhilash C. B. JSSATE, Bengaluru Anil B. C. JSSATE, Bengaluru Organizing Committee Bhavani B. H. Rajeshwari K. S. Shanthala K. V. Pooja H. Manjunath Talawar Niranjan K. C. Shwetha Kaddi Savita S. Pradeep H. K. Mahesh Kumar Sreenatha Renuka Rajendra Rohitaksha Rashmi B. N. Vinutha H. D. Prasad M. R. Sangeetha H. S. Sumathi H. R. Organization IX
- 13. Mamatha G. Apsara M. B. Nagamani N. Purohit Rekha P. M. Nethravathi Anitha P. Sudha P. R. Sowmya K. N. Fathima Afroz Nagashree S. Sahana V. X Organization
- 15. A Minimal Cognitive Model for Translating and Post-editing Michael Carl Centre for Research and Innovation in Translation and Translation Technology, Copenhagen Business School, Frederiksberg, Denmark mc.ibc@cbs.dk This study investigates the coordination of reading (input) and writing (output) activities in from-scratch translation and machine translation post-editing. We segment logged eye movements and key logging data into minimal units of reading and writing activity and model the process of post-editing and from-scratch translation as a Markov model. We show that the time translators and post-editors spend on source or target text reading predicts with a high degree of accuracy how likely it is that they engage in successive typing. We further show that the typing probability is also conditioned by the degree to which source and target text share semantic and syntactic properties. The minimal Markov model describes very basic factors which are suited to model the cognitive processes occurring during translation and post-editing.
- 16. Human Cognition Inspired Learning Strategies for Particle Swarm Optimization Algorithm Narasimhan Sundararajan Nanyang Technological University, Singapore ENSUNDRA@ntu.edu.sg These days, the nature of global optimization problems especially for engineering sys- tems have become extremely complex and it is difficult to locate the true optimum solutions. For these types of problems, finding the optimal/near-optimal solution in a quick and efficient way is very important and here only search based methods are found to be effective. Among the search-based methods, nature inspired optimization algo- rithms are providing much better solutions. A well-known nature inspired method, the Particle Swarm Optimization (PSO) algorithm has been mostly preferred due to its simplicity and ability to provide better solutions and it has been proven to be more effective for solving complex real-world problems. The limitations associated with PSO have been extensively researched and different modifications, variations and refinements to PSO have been proposed for enhancing the performance of the algorithm. These include parameter tuning, neighbourhood topologies and unique learning strategies. The PSO variants with unique learning strategies are found to be more effective in enhancing the convergence characteristics of the basic PSO algorithm. All these variants have utilized the behaviour of the swarm which limited the usage of intelligence and motivated us towards exploring human cognitive learning principles for PSO. As discussed in learning psychology, human beings are known to be intelligent and have good social cognizance. Therefore, any optimization technique employing human-like learning strategies should prove to be more effective. In this talk, first the basic PSO and its variants will be presented to show the status of current work. Then the human cognition inspired learning strategies will be introduced to address the limitations of PSO and enhancing its convergence characteristics. By mimicking the human-like behaviour, the PSO algorithm has shown faster convergence and closer to the optima over diverse set of problems being a potential choice for complex real-world applications. Recent developments in this area undertaken by our group will be highlighted.
- 17. Speech Processing: Human Cognition vs Cognitive Computing S. R. Mahadeva Prasanna Indian Institute of Technology, Guwahati, India prasanna@iitg.ac.in Cognitive computing tries to address challenging problems. These include processing natural data like speech. Human cognition has remarkable ability to perform spoken communication. Most of the speech processing tasks are trivial for human cognition. Alternatively, they prove to be harder for cognitive computing. This talk will explain some speech processing talks to bring the challenges in the field of cognitive com- puting for speech processing.
- 18. Data Stream Analytics in Complex Environments Mahardhika Pratama Nanyang Technological University, Singapore mpratama@ntu.edu.sg The era of big data in highly complex environments calls for algorithmic development of advanced machine learning techniques and visualizations to transform massive amounts of information into useful references to help decision making process in real-time. This talk aims to discuss online real-time strategies for data stream analytics that provide concrete solutions to unsolved issues in data streams analytics, namely uncertainty in data distribution, uncertainty in data representation, uncertainty in data dimensions, uncertainty in data processing, and uncertainty in data visualization.
- 19. Understanding Brain Effects of Neuro-Psychiatric Disorders via MRI Vigneshwaran Agency for Science Technology and Research (A*STAR), Singapore vigneshwaran_subbaraju@sbic.a-star.edu.sg Neuroimaging technologies, especially functional and structural MRI, are increasingly being looked upon as tools capable of revealing the complex mechanisms in the human brain that underlie several neuro-psychiatric conditions. Thus, they have the potential to influence not just psychiatric diagnosis but also therapy and treatment. The main stumbling block in translating these tools from the laboratory to the mainstream clinical environment is the dearth of sufficiently large datasets to derive generalizable and reproducible conclusions. This is being addressed by the research community, by collating and releasing disorder specific datasets for open research. Few examples are, the ABIDE, ADHD-200, ADNI and the SchizConnect initiatives. The release of these large datasets presents an unprecedented opportunity to perform large scale studies and uncover the neural mechanism underlying these conditions. The adoption of the analysis methods in the mainstream clinical process will depend on two factors: accuracy and interpretability. The emphasis on clinical interpretability is so significant that the analysis has so far been restricted to simple statistical tools. In this talk, I will discuss the application of spatial filtering to fMRI analysis that can help achieve high accuracy and also provide better interpretability. Based on the large datasets in ABIDE and ADHD-200, I will also show the how these methods can be used touncover potential compensatory effects within the brain as well.
- 20. Predictive Analysis for Personalized Clinical Decision Support Savita Ramasamy Institute for Infocomm Research, Data Analytics Department, Singapore ramasamysa@i2r.a-star.edu.sg Public healthcare providers are faced with the challenge of providing quality care at lesser cost. The availability of huge heterogenous data sets available in such healthcare settings provide a wide scope for the development of clinical decision support using predictive analytics and data mining tools. Existing clinical decision support tools do not address the individual needs of patients, and often do not translate to care decisions that clinicians can leverage on. This talk will focus on our recent work in the devel- opment of a predictive analytics solution for a personalized clinical decision support tool. The decision support tool comprises of two main components, namely, neural network predictor to predict the readmission risk for patients and the cost of care, and a multi-objective optimization that optimizes the risk and cost based on the model-based outcomes, constrained based on the desired clinical outcomes. Preliminary results from the study will also be discussed.
- 21. Contents Cognitive Computing in Medical Information Processing Eyes Open and Eyes Close Activity Recognition Using EEG Signals . . . . . . 3 Barjinder Kaur, Dinesh Singh, and Partha Pratim Roy Analysis of Breast Thermal Images Using Anisotropic Diffusion Filter Based Modified Level Sets and Efficient Fractal Algorithm . . . . . . . . . . . . . 10 S. Prabha, S. S. Suganthi, and C. M. Sujatha An Empirical Evaluation of Savitzky-Golay (SG) Filter for Denoising ST Segment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 C. K. Roopa and B. S. Harish Detection of Exudates Through Local Binary Pattern in Diabetic Retinopathy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 R. Suma, Deepashree Devaraj, and S. C. Prasanna Kumar Automated Lung Parenchyma Segmentation in the Presence of High Attenuation Patterns Using Modified Robust Spatial Kernel FCM . . . . . . . . . 40 Shyla Raj, D. S. Vinod, and Nagaraj Murthy A Heuristic Approach to Automatically Segment Signal from Background in DNA Microarray Images . . . . . . . . . . . . . . . . . . . . . . 51 S. S. Manjunath, Priya Nandihal, and Lalitha Rangarajan Filter Based Approach for Automated Detection of Candidate Lung Nodules in 3D Computed Tomography Images. . . . . . . . . . . . . . . . . . . . . . 63 K. Bhavanishankar and M. V. Sudhamani Liveness Detection Based on Eye Flicker. . . . . . . . . . . . . . . . . . . . . . . . . . 71 Rekha A. Shidnekoppa, Manjunath Kammar, and K. S. Shreedhar Influence of Health Service Infrastructure on the Infant Mortality Rate: An Econometric Analysis of Indian States . . . . . . . . . . . . . . . . . . . . . . . . . 81 C. Arun, Sangita Khare, Deepa Gupta, and Amalendu Jyotishi Health and Nutritional Status of Children: Survey, Challenges and Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Sangita Khare, Deepa Gupta, K. Prabhavathi, M. G. Deepika, and Amalendu Jyotishi
- 22. An Empirical Analysis of Machine Learning Classifiers for Clinical Decision Making in Asthma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 M. R. Pooja and M. P. Pushpalatha Web Based Blood Donation Management System (BDMS) and Notifications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 B. M. Shashikala, M. P. Pushpalatha, and B. Vijaya Silent Speech Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 Amaresh P. Kandagal, V. Udayashankara, and M. A. Anusuya Cognitive Computing and Its Applications Swarm Intelligent Approaches for Solving Shortest Path Problems with Multiple Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Jinil Persis Devarajan and T. Paul Robert Improved Directionally Driven Self-regulating Particle Swarm Optimizer . . . . 157 Saumya Jariwala Judgement of Learning for Metacognitive Type-2 Fuzzy Inference System . . . 170 Khyati Mahajan Inter Intensity and Color Channel Co-occurrence Histogram for Color Texture Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 S. Shivashankar, Madhuri R. Kagale, and Prakash S. Hiremath LDA Based Discriminant Features for Texture Classification Using WT and PDE Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Rohini A. Bhusnurmath and P. S. Hiremath Position Error Analysis of IRNSS Data Using Big Data Analytics. . . . . . . . . 201 M. Geetha Priya and D. C. Kiran Kumar Cluster Representation and Discrimination Based on Regression Line . . . . . . 210 M. S. Bhargavi and Sahana D. Gowda Recognition of Traffic Sign Based on Support Vector Machine and Creation of the Indian Traffic Sign Recognition Benchmark . . . . . . . . . . 227 Vidyagouri B. Hemadri and Umakant P. Kulkarni Neural Network Based Characterization and Reliable Routing of Data in Wireless Body Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 Biradar Shilpa, S. G. Hiremath, and G. Thippeswamy Automata Approach to Reduce Power Consumption in Smart Grid Cloud Data Center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 J. Usha, S. R. Jayasimha, and S. G. Srivani XX Contents
- 23. Hybrid Optimization in Big Data: Error Detection and Data Repairing by Big Data Cleaning Using CSO-GSA. . . . . . . . . . . . . . . . . . . . . . . . . . . 258 K. V. Rama Satish and N. P. Kavya Application of Optimization Technique for Performance and Emission Characteristics of a CNG-Diesel Dual Fuel Engine: A Comparison Study . . . 274 A. Adarsh Rai, B. R. Shrinivasa Rao, Narasimha K. Bailkeri, and P. Srinivasa Pai Use of Hybrid Algorithm for Surface Roughness Optimization in Ti-6Al-4V Machining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 Grynal D’Mello, P. Srinivasa Pai, and Adarsh Rai Eigenvalue Analysis with Hough Transform for Shape Representation and Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Bharathi Pilar and B. H. Shekar Event Data Analysis in Large Virtualized Environment . . . . . . . . . . . . . . . . 313 M. B. Bharath and D. V. Ashoka Noisy Speech Recognition Using Kernel Fuzzy C Means. . . . . . . . . . . . . . . 324 H. Y. Vani and M. A. Anusuya An Enhanced Water Pipeline Monitoring System in Remote Areas Using Flow Rate and Vibration Sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . 331 Praveen M. Dhulavvagol, K. R. Ankita, G. Sohan, and Renuka Ganiger Smart Helmet with Cloud GPS GSM Technology for Accident and Alcohol Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346 Praveen M. Dhulavvagol, Ranjitha Shet, Prateeksha Nashipudi, Anand S. Meti, and Renuka Ganiger Text-Dependent Speaker Recognition System Using Symbolic Modelling of Voiceprint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358 Shanmukhappa A. Angadi and Sanjeevakumar M. Hatture An IOT Based Smart Shopping Cart for Smart Shopping . . . . . . . . . . . . . . . 373 Srinidhi Karjol, Anusha K. Holla, and C. B. Abhilash Pathnet: A Neuronal Model for Robotic Motion Planning . . . . . . . . . . . . . . 386 V. M. Aparanji, Uday V. Wali, and R. Aparna Impact of Named Entity Recognition on Kannada Documents Classification. . . 395 R. Jayashree, Basavaraj S. Anami, and S. Teju Big Data Analysis - An Approach to Improve Power System Data Analysis and Load Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Sandhya S. Shankarlinga, K. T. Veeramanju, and R. Nagaraja Contents XXI
- 24. Backward – Forward Algorithm Approach for Computation of Losses in LVDS and Proposed HVDS - Towards Loss Minimization and Voltage Improvement in Agricultural Sector. . . . . . . . . . . . . . . . . . . . . 415 G. B. Prakruthi and K. T. Veeramanju Analysis of Segmentation Methods on Isolated Balinese Characters from Palm Leaf Manuscripts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430 Deepak Kumar, K. Vatsala, Sushmitha Pattanashetty, and S. Sandhya Energy Harvesting from Dairy and Hospital Wastewater Using Microbial Fuel Cell (MFC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 440 C. Shakunthala and Surekha Manoj Cognitive Computing in Video Analytics Anomalous Event Detection in Videos Using Supervised Classifier . . . . . . . . 449 K. Seemanthini and S. S. Manjunath Classification and Clustering of Infected Leaf Plant Using K-Means Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468 Prathyakshini, Akshaya, and C. V. Aravinda Human Action Detection and Recognition Using SIFT and SVM . . . . . . . . . 475 Praveen M. Dhulavvagol and Niranjan C. Kundur Novel Real-Time Video Surveillance Framework for Precision Pesticide Control in Agribusiness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 492 Nayana G. Bhat and Guruprasad M. Bhat Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501 XXII Contents
- 25. Cognitive Computing in Medical Information Processing
- 26. Eyes Open and Eyes Close Activity Recognition Using EEG Signals Barjinder Kaur1(✉) , Dinesh Singh1 , and Partha Pratim Roy2 1 DCRUST, Sonepat, India kaur.barjinder@gmail.com, dinesh.madhav@gmail.com 2 IIT Roorkee, Roorkee, India proy.fcs@iitr.ac.in Abstract. So far Electroencephalography (EEG) has been analyzed by the re- search community for interaction with the computers. Studies regarding EEG signals has gained attention in the recent past as it gives an alternate way of com- munication for the persons suffering from partially or fully paralytic disability. Every second different activities are performed by millions of neurons. Decoding and detecting such complex activity of the brain while analyzing the EEG signals is a challenging task. In this paper, we have proposed an activity recognition system using EEG signals. The two activities, namely, eyes open (EO) and eyes close (EC) have been considered in this work. The recorded signals are then decomposed using Discrete Wavelet Transform (DWT) to analyze the impact of both the activities. The recognition of activities has been performed using Support Vector Machine (SVM) classifier. For experimentation, a publicly available dataset i.e. PhysioNet consisting data of 109 users while performing one minute EO and EC activity has been used. A notable activity recognition rate of 86.08% has been recorded using gamma band feature. The paper further proposes that the system can be used as a reference to detect different types of activities performed at different instance of time and for rehabilitation purposes also. Keywords: Electroencephalography (EEG) · Eyes open (EO) · Eyes close (EC) Discrete Wavelet Transform (DWT) · Support Vector Machine (SVM) 1 Introduction Brain Computer Interface (BCI) is a mechanism of acquiring the neuro signals. These acquired signals store mirage of information that has been proved helpful in diagnosing various diseases, and providing help for motor-disabled people. Various methods like Electroencephalogram (EEG), Magnetoencephalogram (MEG), functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET), etc. are available to capture the brain functionality. However, taking into account the cost and easy port‐ able aspect, EEG has been widely used by researchers [5, 6, 14] as it provides a non- invasive monitoring method to analyze the brain recordings, and is frequently used to implement different BCI systems. The brain oscillations are usually measured between 0.5–40 Hz frequency range. © Springer Nature Singapore Pte Ltd. 2018 T. N. Nagabhushan et al. (Eds.): CCIP 2017, CCIS 801, pp. 3–9, 2018. https://doi.org/10.1007/978-981-10-9059-2_1
- 27. Thus, based on these measurements EEG signals are segregated into five rhythms, namely, delta (0.5–<4 Hz), theta (4–7 Hz), alpha (8–15 Hz), beta (16–32 Hz) and gamma (32 Hz above) that are omnipresent in different parts of the brain [5]. EEG helps in detecting the various abnormalities of the brain like sleeping disorders, emotional variance, seizures detection, and in the recent past researchers have been widely using EEG signals for proposing user identification systems [5, 6], Neuromarketing [14], rating prediction systems [4] and envisioned speech recognition [7]. Although normal EEGpatterns are easilyrecognizable, but to evaluate the reason of change in EEG signals is a cumbersome task. The human brain consists of about 86 million neurons which helps in controlling all the conscious and unconscious daily decisions [3]. Recognizing and detecting the activity that could have lead to abnormal behavior of the neuro signals has made the researchers to analyze the activity happened during the course of action. Saghafi et al. [11] used power spectrum, variance, maximum, and range features to discriminate between the eye state change. The authors have used Logistic Regression and Artificial Neural Network (ANN) classifier to evaluate the system performance whereas an auto regressive power feature has been used to discriminate between eyes close and eyes open state. High frequency power has been reported in frontal, parietal, and occipital areas in eyes close state. Öner et al. [8] investigated the moments of the eyes using one-channel. The authors focused on the ocular movements of the eye. The features were selected using analysis of variance (ANOVA) function and a linear fit model has been used to report the higher presence of delta rhythm while performing the task. However, their study was conducted with EEG data of 2 subjects. In [13], the authors have analyzed left and right hemisphere of the brain using EEG signals recorded in eyes open state. Occipital lobe and left hemisphere were found more active by analyzing alpha band waves. Qidwai et al. [10] analyzed the data of six subjects to discrimi‐ nate the two motor activities EO and EC. Different angle features of the eye states were used for feature extraction and fed directly into a Multilayer Perception (MLP) based Neural Network classifier for recognition purpose. The authors in [2] have proposed a methodology to detect the left and right fist motor movement activity using EEG signals of 6 subjects only. The signals were filtered with a notch filter and were further preprocessed using Automatic Artifact Removal (AAR) technique. They have used DWT based signal decomposition technique and three different features, namely, power, mean and energy were extracted for classification purpose. Simi‐ larly, the classification of imagined left and right fists movement was proposed in [1] using EEG signals of 20 users. Root Mean Square (RMS) and Mean Absolute Value (MAV) features were extracted and modeled using SVM and ANN classifiers where accuracies of 84.5% and 82.1% were recorded from 3 channels, respectively. Accurate recognition of the performed activity by analyzing EEG signals not only proves helpful in healthcare but also contributes towards self-evaluation of changes in daily health parameters. Therefore, in this paper, we analyzed the neuro signals for activity recognition. For this, two activities, namely, eyes open (EO) and eyes close (EC) are considered. Figure 1 depicts the flow diagram of the proposed approach, where brain signals are recorded in one of the two states i.e. EO and EC. The recorded signals are then preprocessed and two features, namely, Standard Deviation (STD) and RMS are 4 B. Kaur et al.
- 28. extracted. Finally, the recognition of activities is performed using Support Vector Machine (SVM) classifier. Fig. 1. Framework for eyes open and eyes close activity recognition using EEG Signals. Rest of the paper is organized as follows. The description of signals preprocessing and feature extraction techniques are presented in Sect. 2. Section 3 presents the exper‐ imental results. Finally, we conclude the paper in Sect. 4 by highlighting some future possibilities of the present work. 2 Signal Preprocessing and Features Here, we present the details of the preprocessing techniques used to filter the raw signal data and the features extracted before classification of the activities. Preprocessing. It has been analyzed that while recording the data, the EEG signals are always contaminated with unwanted noise from the surroundings, hair, ocular move‐ ments etc. To get the enhanced performance of the system these artifacts need to be removed. Bio-signals being non-stationary in nature tends to get distorted due to sensi‐ tiveness of the device. Also, it has been researched that the human brain activities are limited upto 40 Hz, where higher value signifies noise. Therefore, to remove such noise and to restrict the frequency till 40 Hz, a low pass filter is used. For this, we have used a Butterworth low pass filter of 5th order to preprocess [9] the EEG signals. The filter can be defined using (1). H(z) = q(1) + q(2)z−1 + … + q(n + 1)z−n 1 + p(2)z−1 + … + q(n + 1)z−1 (1) where n denotes the filter order with normalized cutoff frequency Wn. It returns the filter coefficients of length n + 1 as row vectors q and p, along with the coefficients z in descending powers. Wavelet Analysis and Statistical Features. As EEG signals are non-stationary, there- fore, DWT provides a flexible way to analyze the signals in time-frequency domain [5, 14]. It is an analysis technique that is based on sub-band decomposition coding pro- viding easy implementation and also helps in reducing the computation time. DWT is Eyes Open and Eyes Close Activity Recognition Using EEG Signals 5
- 29. widely used in applications including emotion detection, security, seizure detection and gaming, etc. In this work, we have used Daubechies-4 (DB-4) wavelet to decompose the EEG signals into five sub-bands, namely, gamma, beta, alpha, theta and delta. Next, two statistical features i.e. STD and RMS have been extracted from the sub-bands. More details about the DWT and features can be found in [5]. 2.1 Activity Recognition Using SVM SVM has been widely used by researchers for the classification of EEG signals. It sepa‐ rates the samples into two different classes using the hyperplane [5]. It transforms the original feature space into a high dimensional space with maximum margin. Different kernel functions are used to perform the mapping by performing calculations in the data space and returns it to feature space. For given training data, (x1, y1), (x2, y2), (xn, yn), the decision function f (x) is defined in (2). f(x) = sgn{(w ⋅ x) + b} = sgn ( ∑ i=1 yiaik < xi, x > +b ) (2) Here (w ⋅ x) represents the dot product between weight vector w and x data with binary class. The parameter b is defined as a scalar that is often referred to as the bias. The term αi denotes the embedding coefficients and k <xi, x> is the kernel represented by the dot product i.e. <., .>. The experimental results are classified by varying kernel functions i.e. linear and Radial Basis Function (RBF). 3 Results Here, the details of the dataset and the results of activity recognition are presented. The computation of the results has been performed using 2-fold cross validation. The details are as follows. 3.1 DataSet Description For activity recognition, a freely online available EEG-based motor movement and imaginary dataset provided by PhysioNet BCI [12] has been used. EEG signals were recorded from all 64 channels for 1 min. Two baseline tasks, eyes open (EO) and eyes close (EC) resting state have been used to collect the data from 109 users and are considered in this work. In order to detect each activity accurately, the EEG data has been segmented into 10 s. Thus, a total of 1308 EEG files (i.e. 654 for EC and 654 for EO) have been created for analysis. 3.2 Activity Recognition Results Using SVM The SVM classifier has been trained for recognizing the activity performed by the users using the extracted features. The accuracies have been computed for all 5 sub-bands. 6 B. Kaur et al.
- 30. The recognition results are depicted in Fig. 2, where the maximum accuracy of 86.08% is recorded on gamma band features. Fig. 2. Activity recognition results on different sub-bands. Recognition results for EO and EC have also been evaluated for the gamma band waves as depicted in Fig. 3, where accuracies of 92.05% and 80.10% are recorded for both the activities, respectively. Fig. 3. Recognition rates of EO and EC activities. A comparative analysis has also been performed by varying SVM kernels. For this, we have trained the SVM with Linear kernel using gamma band features and compared the performance with RBF kernel for activity recognition purpose. The comparison is depicted in Fig. 4, where the proposed RBF kernel based activity recognition outper- form the other by a margin of 14.37%. Eyes Open and Eyes Close Activity Recognition Using EEG Signals 7
- 31. Fig. 4. Comparative performance analysis with linear kernel based SVM. 4 Conclusion The human brain is a complex structure. Therefore, recognizing the activity performed by the user is a challenging task. As eyes open (EO) and eyes close (EC) are the most commonly used baseline activities that users perform while recording EEG signals. In this paper, we have proposed an activity recognition framework for the two activities using EEG signals. DWT based signals decomposition technique has been used to extract statistical features. An online freely available dataset Physionet has been used in this work that consists EEG signals data of 109 users. The recognition of activities has been performed using SVM classifier achieving an accuracy of 86.08%. In future, more activities can be recognized using the proposed framework that will prove helpful for various healthcare applications. References 1. Alomari, M.H., Baniyounes, A.M., Awada, E.A.: EEG-based classification of imagined fists movements using machine learning and wavelet transform analysis. Int. J. Adv. Electron. Electr. Eng. 83–87 (2014) 2. Alomari, M.H., Samaha, A., AlKamha, K.: Automated classification of L/R hand movement EEG signals using advanced feature extraction and machine learning. Int. J. Adv. Comput. Sci. Appl. 4, 207–212 (2013) 3. Azevedo, F.A., Carvalho, L.R., Grinberg, L.T., Farfel, J.M., Ferretti, R.E., Leite, R.E., Lent, R., Herculano-Houzel, S., et al.: Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J. Comp. Neurol. 513, 532–541 (2009) 4. Gauba, H., Kumar, P., Roy, P.P., Singh, P., Dogra, D.P., Raman, B.: Prediction of advertisement preference by fusing EEG response and sentiment analysis. Neural Networks 92, 77–88 (2017) 5. Kaur, B., Singh, D., Roy, P.P.: A novel framework of EEG-based user identification by analyzing music-listening behavior. Multimed. Tools Appl. 76, 1–22 (2017) 8 B. Kaur et al.
- 32. 6. Kumar, P., Saini, R., Roy, P.P., Dogra, D.P.: A bio-signal based framework to secure mobile devices. J. Netw. Comput. Appl. 89, 62–71 (2017) 7. Kumar, P., Saini, R., Roy, P.P., Sahu, P.K., Dogra, D.P.: Envisioned speech recognition using EEG sensors. Pers. Ubiquit. Comput. 22(1–15), 2017 (2017) 8. Öner, M., Hu, G.: Analyzing one-channel EEG signals for detection of close and open eyes activities. In: International Conference on Advanced Applied Informatics, pp. 318–323 (2013) 9. Palaniappan, R., Ravi, K.: A new method to identify individuals using signals from the brain. In: International Conference on Information, Communications and Signal Processing, vol. 3, pp 1442–1445 (2003) 10. Qidwai, U.,khazaalShams, W.: A source-discriminationapproach fordetection of ASD using EEG data. Int. J. Biosci. Biochem. Bioinf. 3, 492–496 (2013) 11. Saghafi, A., Tsokos, C.P., Goudarzi, M., Farhidzadeh, H.: Random eye state change detection in real-time using EEG signals. Expert Syst. Appl. 72, 42–48 (2017) 12. Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI 2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004) 13. Trivedi, P., Bhargava, N.: Comparing alpha wave activity of left and right hemisphere of brain recorded using EEGlab. Int. J. Sci., Eng. Technol. Res. 6, 170–174 (2017) 14. Yadava, M., Kumar, P., Saini, R., Roy, P.P., Dogra, D.P.: Analysis of EEG signals and its application to neuromarketing. Multimed. Tools Appl. 76(18), 19087–19111 (2017) Eyes Open and Eyes Close Activity Recognition Using EEG Signals 9
- 33. Other documents randomly have different content
- 34. prorsus alienos ab omni pietate, humanitate, dementia, et induat eos crudelitate et malicia diabolica. Talis enim hie Laban describitur, qui avaritia adeo 35 ferus, adeo crudelis et äoTogyos redditur, ut ne ungulam quidem de toto grege libens concedat genero et filiabus. Haec est sententia huius pacti, in quo simul immanis avaritia Laban amplificanda et exagitanda est. lam et textum videamus, qui, ut dixi, propter Eclipsim verborum est obscurior. Primum dicit se separaturum 40 omne pecus punctatum, etiamsi unum tantum punctum habeat. Ut si nigro pecori unus aliquis punctus albus sit in fronte aut alibi: vel album pecus hal)eat unicam nigram notam, quacunque parte corporis. Et Lac 7
- 35. 688 Sotlcfungcn über 1. TOofe üon 1535—45. appellatione videtur compraehendere tanquara sub genere, vel per synecdochen omnia pecora, nou una tanhim, sed pluribus et quidem parvis maculis sive punclis respersa. Hoc enim Hebraeis est pecus Nakocl. Deinde maculosa pecora dicit {Tlialn) quae nos proprie reddimus, bunte. Sic eiiim diseernmitiir hae duae appellationes. Haee sunt notata s maculis graudioribus et latioribus, groffe fitctte ^)le^ ^ duorum colorum, albi aut nigri, lila sunt respersa maculis minoribus. Tertio pecus rufum in agnis segregaturum se ait, hoc est, rufos agnellos, Differunt autem {Schch et Kaemef) illud generis nomen est conveniens omnibus aliis pecudibus. Hoc significat agnum anniculum aut minorem. Quando excessit annum, nun lu vocatur amplius Kucsacf. Nos paschalem agnum dicimus, ein Ofterlemlein. Quarto: etiam in caprarum grege maculosas ot punctata« separat, perinde ut in ovibus. Sed ex agnellis tantum rufos deligit. Sequitur auteui : ' Tdque erit merces mea.' Hie eclipsis est, et est subintelligendum. Quicquid erit hinc natum istis simile, et maculosnm ex is albis. Yidetur enim miln relictum esse lacob tantum alborum gregem, quanquam alii etiam nigras pecudes additas putant. Et est sententia: Quicquid ex albis nascetur varium et sparso vellere, hoc erit meum. Tuum vero, quod natum fuerit album. Est igitur admodum iniqua conditio, siquidem conti-a naturam est ex albis capris aut ovibus generari rufas aut 20 maculosas. Tarnen eam offert homini avaro : Tu enim, inquit: respondebit mihi iustitia mea, id est. ero iustus. Quia pendet haec pactio ex Fortuna at casu, si forte eveniat, ut ex albis mihi nascantur pecudes maculosae, ne quid insidiose aut fraudulenter a me fieri suspiceris, quanquam nullam unquani tibi feci iniui'iam. Porro quicquid non varium, sed unius coloris 25 et album apud me inventum fuerit, id furto tibi surreptum dicito. et libere me furti arguito. Valde dura obligatio est viri tam sancti et iusti apud nebulonem impurissimum. Et apparet inde, Laban saepe conviciis incessisse lacob, si quando non satisfactum fuit cupiditati et avaritiae ipsius, tanquam furto 30 aliquid ablatum esset, aut familia lacob mactasset et comedisset oves, capras etc. de grege. Aut saltem eiusmodi increpationibus et conviciis intermiriari et cavere voluit, ne
- 36. quid in suuni commodum verteret laoob: Tametsi de fide eius et diligentia non posset dubitare. 30,34 Dixitque Laban: Age, utinam fiat iuxta verbum tuum. 35 Videtur Laban adhuc dubitare et diffidere lacob. Utinam, inquit, vel modo fiat, quod dixisti. Age experiamur, donec videam, an respondeat foetura meae expectationi. Quanquam suae avaritiae accommodata omnia videt: tarnen nondum pro rato accipit: sed cum conditione, ur possit 3 Nakod] "ip: 1 ( Thata)] XlVu S/9 {Sclieh et Kaemef)] nc et 3B3 ') = Flecken.
- 37. SBotleiunaeii übet 1. SKofe Aap. 30, 32 -34. 6^9 mutare et rescindere pactum, quando aut quotios velit. Slcut infra audiemiis decoiu vicibus mutatum esse. Augescit igitur magis ac magis detestatio avaritiae Laban. Quia ringitur adhuc, cum offert gener aequissimam couditionem et sibi iniquisaimam. Non adsentitur liquido: Non ideo, quin 5 utilem sibi et amplectendam statueret. sed ut retineat potestatem, ad quemlibet eventum retraetandi et recusandi. Viden, avaritia quid facit? Praeclare igitur a Poeta dictum est^: Auri sacra fames, quid non mortalia cogis pectora? Ita enim mutantur homines, ut niiiil humani rotineant, sed fiant statuae et simulachra sine omni sensu humanitatis. Et 10 pulchre huc accommodatur id, quod Psalra[0 1 1 1} [!]. dicitur: 'Simulachrn uf- iis, 4. ( gentium argentum et aurum, similes illis fiant, qui faciuiit ca, et omnes, qui confidunt in eis.' Usurarii, raptores, avari non sunt homines. 'Oculos tü.ns, sf. habent, et non vident. Aures habent, et non audiunt.' Et Ecclesiast[ico 10. Sit. iu,7 scriptum est: Avaro nihil esse scelestius. Sunt enim homines äarogyoi, i.i immanes. crudeles, qui si omnium hominum vitam obulo possent rcdimere, non darent: Sunt igitur latrones et homieidae, quia rapiunt ea et vorant, quibus alii frui et sustentari deberent. Quod si vel exigua scintilla humanitatis in Laban fuisset reliqua, debuisset flecti ad humanitatem et beneficentiam virtute et liberalitate 20 generi, et cogitare: Video generum meum ingenio bono et honesto piaeditum, et adeo mearum rerura studiosum, ut conditionem non modo tolerabilem, sed et maxime quaestuosam offerat; Sed avertat Deus, ut eam accipiam. Habet filias meas uxores, cum quibus honeste et amanter vivit. et fideliter mihi quatuordecim annis serviit. Nae ego crudelior fuerim 25 quavi^ bestia. Si mea commoda ex eius compararem incommodis: Sed nihil herum ne pei* somnium quidem cogitat, quia avaritia omnem humanitatem, verecundiam et ipsara naturam hominis extinguit, et efficit simulachra argenti et auri etc. Itaque valde mihi placet Poetarum fabula de Mida rege, cuius 3u attactu scribunt omnia in aurum vera esse, quemadmodum voto optaverat fieri. Tales nimirum sunt avari instar statuarum argentearum aut aurearum, sine ullo sensu humanitatis. Et praeclare alibi dictum est
- 38. avarum nihil recte facere, nisi cum moritur.- Sunt enim inutilia, imo noxia pondera terrae. Et toUendi erant ex omnibus Rebuspublicis bene constitutis. Quia 3i sunt pestes soeietatis humanae, et exempla, imo portenta horribilia inliumanitatis, cnidelitatis et impudentiae. ■) Virgüim Aeneis 3, 56 f. -) cfr. Publilius Syrus 23: Avaius nisi cum moritur, nil recte facit. Vgl. Logau: ©in ©ciä'^QU iinb ein fetteS ©ctitoein un§ etft im lobe m^üd) jein. ßutietS äBerfe. -Xl.lll *4
- 39. 690 Sotleiiingctt über 1. IlJofe Don 1535-45. 30,.i5.36Separavit igitur eadeni die hircos notis et maculis respersos, omnesque capras punctatas et maculosas. omnes. inquaiii. in quibus albedinis aliquid erat. Deinde quiequid erat nigruin in agnis, et dedit eas siib manus filiorum suoruni. Posiiit vero spacium itineris trium dierum inter se et inter lacob, qui ■■■ pascebat reliquos gregea Laban. Moses hie aliis nominibus iititur, quam antea. Supra dixit: rufum pecus in agnis. Item punctatum in nvibus et capris. Hie hircos nominat, ut significet ingentem et inexplebilem cu])iditatem Laban, qui etiam eas pecudes segregarit, quae non fuerunt noniinatae in pacto. Quanquam enim i» id per Syriecdoehen supra fieri diximus, tamen non temere videtur diversis appellationibus priores mutasse {Ekudim) Hircos maiores signiticat, qui praecedunt gregem. Sicut (Thaischim) Hircos minores sive capreolos, qui aluntur ad escam, non ad foeturam. Etymologiam vocis {Ekudim) non scio. (Akad) significat vincire. Ideo i.-. exponunt pro hircis. qui habent circuhim album in nigris pedibus, aut econtra, quo quasi revincti pedes videntur. Aut. quod mihi magis phxcet, eos intelligit, qui per dorsum habent maculas oblongas, ein laugen ftnemen *, cuiusmodi fere omnes hirci habent. Ut hoc quoque conveniat ad amplificandam avaritiam Laban, qui exquisitissimo separavit etiam eos. quorum 20 maculae vix notari poterant. Ideo enim dicit: Omne, quod erat in eis album, et omne nigrum in ovibus, quasi diceret: Cum iam pecudes sparsis pellibus separasset, hane curam quoque adhibuit, ut in singulis, quae unius coloris erant, etiam pilos dispiceret in podibus aut barba, si qui essent diverso colore notati. Tarn anxia et solicita segregatione usus est erga 25 ■filias et generum, ut declararet animum sordidissimura omnium mortalium, et Dei odio dignum. Atque hactenus de pacto. 30,.S7— ssPorro lacob accepit virgas de populo viridi. de corilo et castanea, et decorticavit eas, ut albedo appareret, denudans scilicet, quod album erat in virgis. Et posuit virgas, quas decorticavit, 30 in canales aquarum ante oculos oviuin, ut potum venientes in aspectu earum concipcrent. Concipiobant itaque oves ante virgas, et pariebant diverso coloro asperaaa, ac punctis et maculis notatas. Audivimus Laban avide et anxie novae
- 40. rapinae inhiasse. Sed quid .is fity üeus monstrat lacob singulare artificium, quo corrigat et mutet naturam. Sumit enim virgas ex tribus arboribus, et eas deglubit, non quod corticem prorsus detrahat, sed ut varietatem quandam colorum albi 12 (Ekudim)] D- i^p? 13 (Thaischim)] n''Bi^n 15 (Akad)] Ipf ') = Slrei/'en.
- 41. The text on this page is estimated to be only 29.23% accurate Süotlefungcn übev 1. Woe Aap. 30, 35— 39. (i;)| et nigri efficiat: ita iit una parte album propter detractum coiticem, altei'k niirruin rclicto cortice appareat. Has ponit in canales aquarios, ut oves ante ocuioss virgaa habeant. c^t in aspectu earum eoiicipiant. Fuit ingeniosa l'liilosophia sive niagia. (pia cff'ecit, ut oves in ardore libidinis, 5 aspectu variaruin virgaruni vaiios foetus procrearent. Atque ita ex grege albo aut nigro nati sunt multicolores et maculosi. Concipiebant, in llebraeo est Jehemu a Jahum: calofaciel)ant se oves ad virgas, id est, coibant. Psal^moSl.; 'In peccatis {Jechmatcni) concaluit, concepit nie mater mea', «(. r.i, 7 id est, ardore libidinis et turpi pruritu carnis conceptus suin. Significatur 10 calor generans, qui ante lapsuiii fuit purus: et ad generationem conditus et necessarius. Sed iam infectus est peccato originali. Non est innoxius ardor, sicut fuit initio, sed est libidine et concupiscentia corruptus: Sic igitur arte atque ingenio, aut magia naturali fallit lacob artem, sive potius malitiam Laban, quam magiam Patres sive usu longiore, sive ex institu15 tione maiorum didicerunt. lauob enim haud dubio ab aliquo Patriarcharum aut instinctu divino per spirituni sauctuin edoctus fuit. Et est res certa, et conveniens cum doctrina medicorum, qui adfirmant, in conceptu oronium animaliuni, non brutorum tantuni. sed et hominum, peculiares formas aut maculas imprimi foetibus. cum ex imaginatione, tum ex variis obiectis 20 animo aut oculis obversantibus. non solum in ipso ardore conceptus, sed etiam post impraegnationcni. Hieronymus et Physici recitant exemplum reginae, quae enixa fuit infantem forma et facie Aethiopis, propter fortem imaginationem Aethiopis depicti in tabella ad lectum. Aliam item narrant fuisse accusatam de 25 adulierio, quod deformis ipsa peperisset formosum infantem, uti'ique parenti et toti generi dissimilem: et damnata esset, nisi Hyppocrates eam liberasset, monens, ut ex ea quaereretur, an iiabuisset pictam aliquam tabellam in cubieulo, cuius aspectu esset delectata, quae cum esset inventa, absoluta est a iudicibus. Sic in facie, oculis. genis, cervice bifantuni sparsas inter30 dum maculas sanguineas aut
- 42. alterius coloris videmus; Quando nimirum gravidae subito aspectu aut terrore rei inusitatae percitae manus illis membris admoverunr. Vidimus hie Witebergae civem facie cadaverosa, qui dixit matrem ferentem uterum subito oblato oculis cadavere ita fuisse exterritam. ut facies foetus in utero cadaveris formam indueret. 3", Idem fieri studiose solet in admissione pecudum et iumentorum. Sicut adfirmat Hieronymus, apud Hispanos generosissimos equos statui ante equas in admissura. ut illis similes pulli procreentur. Ideo non est iocandum cum praegnantibus, sed dUigens eura earum habenda est propter foetum. Infinita enim sunt pericula abortuum, monstrorum, deformitatum 40 variarum. Maritus igitur tum maxime 'secundum scientiam', ut inquiti*ctrt3,7 7 Jehemu a Jaham] man- a on^ S {Jechmateni)] "^^nsri^ 44*
- 43. 692 Sßotlcf Uligen übet 1. OToic üpii 1535—45. Petrus: oiiin uxorc hubiteh Memini nie puero Isenaci formosam et pudicam inatronain eniti glirem: quod eo iiccidit, (juia ex viciuis aliquis gliri suspenderat iiolam, ad cuius sonitum reliqui fugaientur. Is occurrit mulieri gravidae, quae ignara rei subito occursu et aspectu gliris ita est conterrita, ut foetus in utero degeneraret in formani bestiolae. Talia exempla nimis s usitata sunt, quando pracgnantes saepe subitis adfectibus et pavoribus concitantur, cum vitae discrimine. Cavendum itaque est, ne accidant eis vehementiores cum corporis, tum animi commotiones. Qui enim gravidarum non habent rationem, nee parcnnt teilen» fuetui, fiunt homicidae et parricidae. Sieut quidam usque i" adeii crudeles sunt, ut etiani verberibus saeviant in gravidas: fortes scilicet et aniniosi in sexuni imbecillem. alioqui vovo ignavissimi. Nani nuper audivimus Prineipem queiidam, multis aliis sceleribus et flagiciis nobileni, in coniugem languentem et adfixani lectulo sti-inxisse gladium. Egregiuni vero heroem, et militem strenuum. Atqui id niinime heroicum: sed flagi- 's ciosum est et turpissimum. Si enim vir es, invenies parem, cum quo congrediaris. Heroes sunt fortes contra fortes, et infirmi erga infirmos. Quid est, quod adversus puerum et mulierem praegnantem pugnam cies. Satis superque satis alioqui periculorum est huic sexui, etiam apud moderatos maritos, a vicinis, a Diabolo, a variis spectris et imaginibus brutorum. ■-
- 44. The text on this page is estimated to be only 29.64% accurate 'Jottcfiniiicu iibci 1. l'fofc fia). .'iO, 37—43. 693 Altera iiiduistria est de verno et autumnali conccptu. Usus enim ost Iaci)b suo artificio in primo, id est, veriiu conceptii, quando ovcs sunt tortiores: tum posuit virgas in canales, ut robustiores footus fiereiit vaiii, qui ad se pertinebant. [n autiimnali autem et serotina admissura oves ."> debiiiores sunt, propteroa quod dcstitiiuntur caloro «olis. quem liabent in vcre. Ideo tum non ])()ncbat virgas, ut securidum naturam nascerentur unius coloris agni, qui uil Laban pertinebant. Vernae autem admissurae idoo sunt meliores, quia fiunt ascendentc sole, quando redit oalor, et omnium herbarum et animalium vires augentur. 10 Curavit igitur lacob, ut haberet oves meliores et fortiores, Vernas scilicet, bic fviiling, Sicut autumnales et serotini dicuntnr bic f^ietling unb iüinter^ fdjaff. ('actcrum haec satis ingeniosa industria, imo calliditas et propuinodum nequitia fuit. Et tarnen ibi quoque aequitatem et moderationem talem servavit lacob, ne moveret suspicionem avaro socero, quod arte li fallerotur. Tn admissura hyberna nihil mutabat, ut et Laban aliquam ])artem retineret, nee in totum spoliaretur. Egrogie igitur circumvontus est, nee ])otuit intelligere, qui fieret, quod foetus verni ederentur sparsis pellibus, et autumnales sequerentur naturam. Cogitavit id fnrtuito aut benedictiono divina fieri. Ideo mutavit pactum, ut infra dicntur. decies, -'11 übi. si unquam antea, maxime insigne et detestandum specimen avaritiae suae aedit. Quia enim videbat vernos foetus meliores et robustiores esse, rescindebat pactum, et deligebat varios. Ibi cum lacob non corrigeret naturam suo artificio, edebantur unius coloris, et iterum obveniebant lacob meliores, rursus igitur fallebatur. et rursus ipse mutabat tertia, quarta, imo -'5 decima vice. 0 turpem et conspuendam ab omnibus avaritiam. Flagellant autem me Rabini nostri, quod male reddiderim hunc locum in translatione germanica.' Addunt enim ipsi tertiam industriam ex eo, quod dicitur posuisse separatum gregem ad oves in grege Laban etc. Id sie interpraetantur, quod maculosos agnos in unum gregem redactos fecerit 3u antecedere gregem ovium Laban, ut sequentes oves
- 45. contemplatione praecedentium variorum alios maculosos foetus ederent. Sed haec orta sunt a pessimis et avarissimis Rabinis, qui sanctissimum Patriarcham ex suo ingenio metiuntur, quasi non fuerit contentus illa magia. Sed etiam in conspectum unicolorium gregum adduxerit maculosas oves, ut non tantum 35 ex variaruni virgarum, sed etiam ex varii gregis aspectu varietatem quandam induerent foetus. Cum tarnen Moses tantum de virgis loquatur, et prorsus contrarium velit, videlicet, quod lacob semoverit gregem varium in alium locum. De agnis enim loquitur, quos segregavit. Sicut hodie etiam agnellos seorsim ponunt, quando matres in pascua ducunt. Et 40 seiuncti fuerunt duo greges spacio itineris trium dierum. Ideo non potuerunt simul esse in uno aliquo loco uterque grex, lacob et Laban. ') Vgl. SU dem ganzen Ahscimitt: Unsre Ausg. Bibel 3, iüOff.
- 46. 694 üüoittimigeii üDcv 1. 3JJofe Uoii 15:35—15. Hac autem industria scribit Moses lacob valde looupletatiim esse. Sive, ut est in Hebraeo. Dirupit vir ille, id est. auctus est supra moduin. Atque haec mihi quidem videtur esse gennana et genuina sententia huiiis loci admodum obscuri, congruens cum iudicio omnium oatholicorum, et sumpta ex collatione consequentium, quae quidem hanc sententiam inagis s illustiabunt et confirmabunt, et ex accurata consideratione circumstantiarum omnium. Quaeritur autem, an possit excusari hoc factum lacob, quod manifesta fraude socerum Fallit. Habet enim quandam speciem avaririae, vel potius furti et rapinae. Cur enim non servat ordinem naturae utruque tempore, >" verno et autumnali? Respondeo: Ex üs, quae supra dicta sunt, et quae sequentur infra. varia et honesta excusatio sumi potest. Primum iure humane excusatur, quod concedit iis, qui serviunt dominis avaris et iniquis, si nihil mercedis solvunt, sed tantum rapiunt, spoliant, expilant, ut vicissim rapiant, quod debetur eis pro mercede: Sed debito modo: ne fiat id cum is detrimento domini. Sic filii Israel spoliaverunt Aegyptum, mercedera accipientes iniustae servitutis, quam Aegypti nondum persolverant. Ad eundem modum lacob servierat quatuordecim annis, et multis miseriis, difficultatibus et molestiis conflictatus t'uerat, et tarnen iusta mercede fraudabatur. Ergo iure ad eum pertinebat hoc, quod rapiebat, etiani jo invito et inscio Domino. Haec una responsio est. Secundo, tametsi est dolus et fallacia, tamen fecit eam lacob autiioritate divina. Quia Angelus apparuit et monstravit ei hanc magiam naturalem et licitam esse. Ex Angelo igitur vel ex sanctis patribus lacob eam didicit, qui magnam rerum experiontiam et cognitionem habuerunt. Quando •„■5 vero iubet Deus sanctos et fidelos vires aliquid facere, id sine uUa dubitatione sanctum est et licitum. Tcrtio, infra dicet: (Nam hoc Caput sine se(]uenti intelligi m)n |)otest) Deus respexit laborera et adflictionem meam. Ibi audiemus, quibus angustiis impulsus fuerit ad hanc fraudem struendam. Quod furto peribat, dicet: uu cogebar solvere: Nisi Deus patris mei Abraham et timor Tsaae adfuisset, nunc quoquo vacuum me ablegasses. Optimus et fidelissimus lacob nullam spem habuit comparcendi aut colligendi peculii alicuius propter
- 47. rapacitatem inexplebilem soceri. Ea profecto non fuit exigua. calamitas. qua motus est ad rapinam hanc, praesertim cum accederet autoritas divina, qua iube- 35 batur hoc facere. Plus enim ex te sensit Laban commodi, dixit Angelus. quam tu ei possis ra])ere. Idee iure potes uti arte et fallacia, ut ab eo aliquid auferas, non raptum, .sed a Deo ipso concessum et datum. Itaque et humano et divino iure et extrema nccessitate excusatus est lacob. Non autem cuivis imitandum hoc exemplum erit, nisi in simili casu. 4u Alioqui enim oculi nequam in factum ipsuni tantum intuentur, posthabitis circunstantiis, et pro exemplo aliis rapinis temore accommodant. Verum
- 48. The text on this page is estimated to be only 24.85% accurate Söotlefungen über 1. 3J?oic Stap. 30, 10-43. 09.') nequaquam id sequeris, nisi per omnia similis fueris lacob, et omnes circunstantiae in caau aimili te impiilcrint. Is onim auxit rem familiärem soceri, vi aervivit tarn duriter avarissimo et rapacissimo fjabaii: qui ne victum quidem sufficientcin ei praebuit, passus est vigilias, famem, sitim, s acstum. frigus iioctu dieque sine ulla compensatione aut premio. Quin insuper cogitavit Laban [lacta mercede cum spoliare, siquidem retinet arbitrium mutandi pactum, quoties vult. Hae circumstantiae diligenter aspiciendac et perpcndendae sunt: tum non accusabitur Taeob avaritiae; neque quisquam etiani tacile exemphim hoc imitari volet; (iuia est heroi10 cum, sicut alia multa in Patriarchis. De heroicis autem factis supra diximus. Aliquando ad regulas se accommodant heroici, aliquando non: Sed lacob hie non peccat contra reguhim: Quia succurrit ei ius naturae et civile, et Christus inquit: 'Dignus est mercenarius mercede sua.' watti). lu.io
- 49. The text on this page is estimated to be only 21.76% accurate ÜJftditrägc uub ÜBeriditigungcii. 3u Sanb 42. Ztii S. 370, 6': — 't'elici culpa' bezieht sich auf die litunjische Sentenz: felis culpa, quae tantum et talem meiuit habere ledemptorem; «//. Forster, Ambrosius, Biscliof ron Mailand 1884. S. 297 Nr. 60. V(jl. Unsre Ausg. Bd. 45, 734 (Nachträge) zu S. 349, 4. [G. B.] 3u SSanb 43. Zu, S. 671 Anm. 3. — Unsre Ausg. Bibel 3, 199f. hat Luther tiähere Betrachtungen über Mandragora (Dudaim, Alraun) angestellt. — Die Judenkirsche, physalis Alkekengi, geliört zur Gattung der Solaneen; die hindber, rubus Idaeus, ist die Himbeere: dagegen heißt Brombeere Chamaemm-us (linzykhpädie der gesamten Pharmazie ed. Moeller und Thoms) [aber auch 'batus (s. Die/fenbach-Wiilcker, brambeere; chamaebatus also = chamaevim-us, da batus = marus eine am Boden hriechende Art, Fuchsbeere, Bocksbeere (Heinsius, Wörterbuch) bezeichnet. 0. B.]. [K. Dr.J Sffltitnar. — $of=®ii^bni({eiei. <4!apiev »Oll (Dfdrübet ^aifilin in ^Pfuüiiigeii (JOUrttcmOcva).
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