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A new fuzzy-based ensemble framework based on attention-based deep learning architectures for automated detection of abnormal EEG. (2024). Yang, ZE ; Li, Shihao.
In: International Journal of System Assurance Engineering and Management.
RePEc:spr:ijsaem:v:15:y:2024:i:12:d:10.1007_s13198-024-02591-6.

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  1. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP (2018b) Automated EEG-based screening of depression using deep convolutional neural network. Comput Methods Programs Biomed 161:103–113.
    Paper not yet in RePEc: Add citation now
  2. Afzali A, Khaleghi A, Hatef B, Akbari Movahed R, Pirzad Jahromi G (2023) Automated major depressive disorder diagnosis using a dual-input deep learning model and image generation from EEG signals. Waves in Random and Complex Media 1–16.
    Paper not yet in RePEc: Add citation now
  3. Albaqami H, Hassan GM, Datta A (2023) Automatic detection of abnormal EEG signals using WaveNet and LSTM. Sensors 23(13):5960.
    Paper not yet in RePEc: Add citation now

  4. Alharbi NS, Bekiros S, Jahanshahi H, Mou J, Yao Q (2024) Spatiotemporal wavelet-domain neuroimaging of chaotic EEG seizure signals in epilepsy diagnosis and prognosis with the use of graph convolutional LSTM networks. Chaos, Solitons Fractals 181:114675.

  5. Bagherzadeh S, Shalbaf A, Shoeibi A, Jafari M, San Tan R, Acharya UR (2024) Developing an EEG-based emotion recognition using ensemble deep learning methods and fusion of brain effective connectivity maps. IEEE Access 12:50949.
    Paper not yet in RePEc: Add citation now
  6. Campos-Ugaz WA, Garay JPP, Rivera-Lozada O, Diaz MAA, Fuster-Guillén D, Arana AAT (2023) An overview of bipolar disorder diagnosis using machine learning approaches: clinical opportunities and challenges. Iran J Psychiatry 18(2):237–247.
    Paper not yet in RePEc: Add citation now
  7. Cao X, Yao B, Chen B, Sun W, Tan G (2021) Automatic seizure classification based on domain-invariant deep representation of EEG. Front Neurosci 15:760987.
    Paper not yet in RePEc: Add citation now
  8. Chowdhury MSN, Dutta A, Robison MK, Blais C, Brewer GA, Bliss DW (2020) Deep neural network for visual stimulus-based reaction time estimation using the periodogram of single-trial EEG. Sensors 20(21):6090.
    Paper not yet in RePEc: Add citation now
  9. Cisotto G (2021) REPAC: reliable estimation of phase-amplitude coupling in brain networks. In: ICASSP 2021–2021 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 1075–1079.
    Paper not yet in RePEc: Add citation now
  10. Dong X, He L, Li H, Liu Z, Shang W, Zhou W (2024) Deep learning based automatic seizure prediction with EEG time-frequency representation. Biomed Signal Process Control 95:106447.
    Paper not yet in RePEc: Add citation now
  11. Khaleghi A, Birgani PM, Fooladi MF, Mohammadi MR (2020) Applicable features of electroencephalogram for ADHD diagnosis. Res Biomed Eng 36:1–11.
    Paper not yet in RePEc: Add citation now
  12. Khaleghi A, Mohammadi MR, Moeini M, Zarafshan H, Fadaei Fooladi M (2019b) Abnormalities of alpha activity in frontocentral region of the brain as a biomarker to diagnose adolescents with bipolar disorder. Clinical EEG Neurosci 50(5):311–318.
    Paper not yet in RePEc: Add citation now
  13. Khaleghi A, Mohammadi MR, Shahi K, Motie Nasrabadi A (2021) A neuronal population model based on cellular automata to simulate the electrical waves of the brain. Waves in Random and Complex Media 34(3):1–20.
    Paper not yet in RePEc: Add citation now
  14. Khaleghi A, Mohammadi MR, Shahi K, Nasrabadi AM (2022) Computational neuroscience approach to psychiatry: a review on theory-driven approaches. Clinical Psychopharmacol Neurosci 20(1):26.
    Paper not yet in RePEc: Add citation now
  15. Khaleghi A, Mohammadi MR, Shahi K, Nasrabadi AM (2023) Possible neuropathological mechanisms underlying the increased complexity of brain electrical activity in Schizophrenia: a computational study. Iran J Psychiatry 18(2):1–7.
    Paper not yet in RePEc: Add citation now
  16. Khaleghi A, Zarafshan H, Mohammadi MR (2019a) Visual and auditory steady-state responses in attention-deficit/hyperactivity disorder. Eur Arch Psychiatry Clin Neurosci 269:645–655.
    Paper not yet in RePEc: Add citation now
  17. Khaleghi N et al. (2024) EEG-based functional connectivity analysis of brain abnormalities: a review study. Inform Med Unlocked 101476.
    Paper not yet in RePEc: Add citation now
  18. Khan IM, Khan MM, Farooq O (2022) Epileptic seizure detection using EEG signals. In: 2022 5th international conference on computing and informatics (ICCI), IEEE, pp 111–117.
    Paper not yet in RePEc: Add citation now
  19. Khuat TT, Gabrys B (2020) A comparative study of general fuzzy min-max neural networks for pattern classification problems. Neurocomputing 386:110–125.
    Paper not yet in RePEc: Add citation now
  20. Kohad N, Ramesh R, Roy R, Irrinki S, Natarajan S (2022) Segment based abnormality detection in EEG recordings. In: 2022 2nd international conference on intelligent technologies (CONIT), IEEE, pp 1–8.
    Paper not yet in RePEc: Add citation now
  21. Kyriaki K, Koukopoulos D, Fidas CA (2024) A comprehensive survey of EEG preprocessing methods for cognitive load assessment. IEEE Access 12:23466.
    Paper not yet in RePEc: Add citation now
  22. Lebal A, Moussaoui A, Rezgui A (2023) Epilepsy-Net: attention-based 1D-inception network model for epilepsy detection using one-channel and multi-channel EEG signals. Multimed Tools Appl 82(11):17391–17413.
    Paper not yet in RePEc: Add citation now
  23. Li Y, Liu Y, Cui W-G, Guo Y-Z, Huang H, Hu Z-Y (2020) Epileptic seizure detection in EEG signals using a unified temporal-spectral squeeze-and-excitation network. IEEE Trans Neural Syst Rehabil Eng 28(4):782–794.
    Paper not yet in RePEc: Add citation now
  24. Liu C et al. (2024) Graph neural networks in EEG-based emotion recognition: a survey. arXiv Preprint at arXiv:2402.01138 .
    Paper not yet in RePEc: Add citation now
  25. López S, Obeid I, Picone J (2017) Automated interpretation of abnormal adult electroencephalograms. MS Thesis, Temple University.
    Paper not yet in RePEc: Add citation now
  26. Lopez S, Suarez G, Jungreis D, Obeid I, Picone J (2015) Automated identification of abnormal adult EEGs. In: 2015 IEEE signal processing in medicine and biology symposium (SPMB), IEEE, pp 1–5.
    Paper not yet in RePEc: Add citation now
  27. Mohammadi MR, Khaleghi A, Nasrabadi AM, Rafieivand S, Begol M, Zarafshan H (2016) EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett 6:66–73.
    Paper not yet in RePEc: Add citation now
  28. Nawaz A, Khan SS, Ahmad A (2024) Ensemble of autoencoders for anomaly detection in biomedical data: a narrative review. IEEE Access 12:17273.
    Paper not yet in RePEc: Add citation now
  29. Nour M, Senturk U, Polat K (2024) A novel hybrid model in the diagnosis and classification of Alzheimer’s disease using EEG signals: deep ensemble learning (DEL) approach. Biomed Signal Process Control 89:105751.
    Paper not yet in RePEc: Add citation now
  30. Oh SL et al (2020) A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput Appl 32:10927–10933.
    Paper not yet in RePEc: Add citation now
  31. Paliwal S, Sharma A, Jain S, Sharma S (2024) Machine learning and deep learning in bioinformatics. In: Bioinformatics and computational biology. Chapman and Hall/CRC, pp 63–74.
    Paper not yet in RePEc: Add citation now
  32. Qiu L, Li J, Zhong L, Feng W, Zhou C, Pan J (2024) A novel EEG-based Parkinson’s disease detection model using multiscale convolutional prototype networks. IEEE Trans Instrum Meas 73(1):14.
    Paper not yet in RePEc: Add citation now
  33. Shankar A, Chakraborty D, Dandapat S, Barma S, Saikia MJ (2024) Attention-based deep learning for epileptic seizure type detection. In: 2024 international conference on advancements in smart, secure and intelligent computing (ASSIC), IEEE, pp 1–4.
    Paper not yet in RePEc: Add citation now
  34. Shanmugam S, Dharmar S (2024) Implementation of a non-linear SVM classification for seizure EEG signal analysis on FPGA. Eng Appl Artif Intell 131:107826.
    Paper not yet in RePEc: Add citation now
  35. Skaria S, Savithriamma SK (2024) Automatic classification of seizure and seizure-free EEG signals based on phase space reconstruction features. J Biol Phys 50(2):181–196.
    Paper not yet in RePEc: Add citation now
  36. Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv Preprint at arXiv:1710.10903 .
    Paper not yet in RePEc: Add citation now
  37. Wong S et al (2023) EEG datasets for seizure detection and prediction—a review. Epilepsia Open 8(2):252–267.
    Paper not yet in RePEc: Add citation now
  38. Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19.
    Paper not yet in RePEc: Add citation now
  39. Yindeedej V et al (2024) A scoping review of seizure onset pattern in SEEG and a proposal for morphological classification. J Clin Neurosci 123:84–90.
    Paper not yet in RePEc: Add citation now
  40. Yıldırım Ö, Baloglu UB, Acharya UR (2020) A deep convolutional neural network model for automated identification of abnormal EEG signals. Neural Comput Appl 32(20):15857–15868.
    Paper not yet in RePEc: Add citation now
  41. Yıldız İ, Garner R, Lai M, Duncan D (2022) Unsupervised seizure identification on EEG. Comput Methods Programs Biomed 215:106604.
    Paper not yet in RePEc: Add citation now
  42. Zhang G, Davoodnia V, Sepas-Moghaddam A, Zhang Y, Etemad A (2019) Classification of hand movements from EEG using a deep attention-based LSTM network. IEEE Sens J 20(6):3113–3122.
    Paper not yet in RePEc: Add citation now
  43. Zoppis I et al (2020) An attention-based architecture for EEG classification. In: BIOSIGNALS, pp 214–219.
    Paper not yet in RePEc: Add citation now

Cocites

Documents in RePEc which have cited the same bibliography

  1. A new fuzzy-based ensemble framework based on attention-based deep learning architectures for automated detection of abnormal EEG. (2024). Yang, ZE ; Li, Shihao.
    In: International Journal of System Assurance Engineering and Management.
    RePEc:spr:ijsaem:v:15:y:2024:i:12:d:10.1007_s13198-024-02591-6.

    Full description at Econpapers || Download paper

  2. Bifurcation dynamics and FPGA implementation of coupled Fitzhugh-Nagumo neuronal system. (2024). Shi, Wei ; Min, Fuhong ; Yang, Songtao.
    In: Chaos, Solitons & Fractals.
    RePEc:eee:chsofr:v:188:y:2024:i:c:s0960077924010725.

    Full description at Econpapers || Download paper

  3. A gazelle optimization expedition for key term separated fractional nonlinear systems with application to electrically stimulated muscle modeling. (2024). Khan, Taimoor Ali ; Chaudhary, Naveed Ishtiaq ; Zahoor, Muhammad Asif ; Mehmood, Khizer ; Hsu, Chung-Chian ; Shu, Chi-Min.
    In: Chaos, Solitons & Fractals.
    RePEc:eee:chsofr:v:185:y:2024:i:c:s0960077924006635.

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