EEG guest lecture_iub_eee541

Electroencephalography
(EEG)
Dr. Md. Kafiul Islam
Dept. of EEE, IUB
1st April, 2017

Outline
 Introduction
 About Brain Recordings
 What is EEG?
 Acquisition Techniques
 Biomedical Electronics! (Analog + Digital)
 Signal Analysis & Processing
 Interferences and Artifacts
 Applications
 Diagnosis of Neurological Disorders
 Brain-Computer Interface / Neural Prosthesis
 Basic Neuroscience, Sleep Study, Depression Study, Biometrics, etc.
 Conclusion
 Future Possibilities
 Limitation & Challenges

Brain Recordings!
 Non-Invasive
 EEG (electrical) and MEG (magnetic)
 Invasive
 Sub-scalp EEG (S-EEG)
 ECoG / iEEG
 Intracortical Implant
 Better signal quality but …!?
Motivation to record brain signals:
 How brain works?
 Information processing
 Memory formation
 Understanding neurological disorders
& providing treatment

What is EEG?
 Recording of the brain's spontaneous electrical activity over a period of time
by placing flat metal discs (electrodes) attached to the scalp.
 Measures voltage fluctuations resulting from ionic current within the neurons
of the brain.
 Scalp-EEG: Most popular brain recording technique
 Low cost
 Non-invasive
 Portable
 Reasonable temporal resolution
The first human EEG recording obtained by Hans Berger in 1924. The
upper tracing is EEG, and the lower is a 10 Hz timing signal.
Electro-encephalo-gram Electrical-Brain-Picture

EEG Acquisition
Standard 10-20 Electrode Montage Acquisition involves
 Electrodes
 Amplification (IA)
 Filtering (Active)
 Digitizing (ADC)
 Storage
Challenge: To detect signal as
low as 20 µV!

 Delta: (< 4 Hz)
◦ adult slow-wave sleep
◦ in babies
◦ continuous-attention tasks
 Theta: (4 - 8 Hz)
◦ higher in young children
◦ drowsiness in adults and teens
◦ idling
 Alpha: (8 - 13 Hz)
◦ relaxed/reflecting
◦ closing the eyes
 Beta: (14 - 30 Hz)
◦ active thinking, focus, high alert, anxious
 Gamma: (> 30 Hz)
◦ Displays during cross-modal sensory processing
 Mu: (7.5 - 12.5 Hz)
◦ Shows rest-state motor neurons
Gamma
•Rhythmic: EEG activity consisting in waves of approximately constant frequency.
•Arrhythmic: EEG activity in which no stable rhythms are present.
•Dysrhythmic: Rhythms and/or patterns of EEG activity that characteristically appear in patient
groups or rarely or seen in healthy subjects.

Artifacts & Interferences in EEG
Sources of Artifacts
 Environmental factors (e.g. power noise,
sound/optical interference, EM-coupling from
earth, etc.)
 Experiment factors (e.g. electrode position
altering, connecting wire movement, etc. due
to mainly subject motion )
 Physiological factors (e.g. EOG, ECG, EMG,
etc.)
Problems with Artifacts
 Can cause electronics saturation [1]
 High dynamic range required (Higher ENOB in ADC) [2]
 Increase false alarms in epileptic seizure
detection [ ]
 Mistakes in BCI classifications
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
x 10
5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
x 10
-3
Time Sample
Voltage,V
[1]
260 265 270 275 280 285 290 295
-15
-10
-5
0
5
x 10
-4
Time, Second
Voltage,Volt
[2]
[ ]

Artifacts Found in EEG* (1)
Horizontal Eye
Movement (T1-T2)
and
Blink Artifacts
(Frontal channels)
EOG 
*Chang, B. S., Schachter, S. C., & Schomer, D. L. (2005). Atlas of ambulatory EEG.
Amsterdam ; London: Amsterdam ; London : Elsevier Academic Press, c2005.

Artifacts Found in EEG (2)
Rhythmic eye flutter can occur at quite high frequencies, in this case 5–8 Hz, and
trigger automated seizure detection algorithms.
This artifact can be distinguished from ictal events in different ways, including the
lack of evolution in frequency or amplitude over time.
Eye Flutter Artifact
recorded by Seizure
Detection Algorithm

Electrode Tapping
Artifact
With ambulatory EEG recording, there may be periods in which the patient
is scratching or pressing on the scalp electrodes.

Jaw-Clenching
Artifact
 Periods of jaw clenching and biting during ambulatory EEG recording can be associated with artifact.
 This activity is often most prominent over the temporal regions, presumably due to temporalis or
masseter muscle contraction.

Chewing Artifact
 Chewing artifact can often have rhythmic features and at times may resemble ictal activity.
 This artifacts are characterized by the rhythmic appearance of muscle artifact centered mostly
over the temporal regions bilaterally.

Chewing Artifact
Recorded by
Spike Detection
Algorithm
 Chewing artifact can have rhythmic and sharp features, and can thus be picked up by
automated detection algorithms.
Here, the spike detections at 23:53:23 and at 23:53:34 capture high frequency sharp waveforms
that are artifactual in nature, in association with a nighttime snack.

Dry Electrode Artifact
 One disadvantage of ambulatory EEG monitoring is the inability of technologists to check on
recording quality frequently in real time and to respond with appropriate technical adjustments as
needed throughout the day, as would typically occur in an inpatient monitoring unit.
 Here, a dry electrode at F8 causes an artifact seen throughout this excerpt.

Forehead Rubbing
Artifact
 As with many other patient movement artifacts, rhythmic rubbing artifact can be a potentially
confusing finding on ambulatory EEG monitoring, particularly in the absence of concomitant video
recording. Here, rhythmic rubbing of the forehead produces an artifact in the anterior channels,
extending from the beginning of this excerpt through 20:50:16.
 Differentiation of such artifact from an ictal event, based on the lack of evolution of the artifact
in frequency or amplitude, is critical.

Pulse Artifact
 Pulse artifact is caused by the rhythmic movement of scalp electrodes, usually over the
temporal regions, by the pulsations of blood through vessels close to the skin, such as the
superficial temporal artery.
 It is to be distinguished from the more common electrocardiographic (EKG) artifact in that pulse
artifact is typically not as sharply contoured, resembles a slow wave, and follows the QRS complex
rather than being simultaneous to it.
 Here, pulse artifact is seen in channels 9 and 10 over the left temporal region throughout this
entire excerpt.

Signal Analysis & Processing Techniques
 Fourier Transform / PSD
 Use of an extra sensor
 Adaptive filtering
 Accelerometer
 Gyroscope
 Contact Impedance Measurement
 A-priori user input required
 Wiener/Kalman/Particle Filtering
 Blind Source Separation
 ICA/CCA/MCA
 Time-series Analysis
 Wavelet transform / STFT
 Empirical Technique
 HHT/EMD/EEMD

Applications
 Epilepsy Diagnosis: Seizure Detection Most
Common
 Also to diagnose sleep
disorders, coma, encephalopathies,
and brain death
 Brain-Computer Interface (BCI) / Neural
Prosthesis
 Basic Neuroscience Research
 cognitive science, cognitive psychology,
and psychophysiological research.

Limitations and Challenges
 Lower spatial resolution
 Lower SNR
 Low degree of freedom for Neural Prosthesis
 Only superficial info from brain’s electrical activity (No access to individual
neuron)
 High EEG channel density provide cross-talk noise
 More prone to Artifacts and Interferences
 Data transmission for wireless EEG
 Real-time EEG processing for high channel density is a major challenge

EEG guest lecture_iub_eee541

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