Interpretation of digital EEG

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1 Interpretation of digital EEG Ki-Young Jung, MD, PhD Dept. of Neurology, Korea University Medical Center, Korea University College of Medicine, Seoul, Korea KU Computational Neuroscience Research Lab (KUCNL) KEP June 7, 2012

2 Digital EEG instrument Memory Filter G1 G2 Amplifier ADC Microprocessor Monitor digitization Pen write unit archives Software: record & interpretation

3 Analog-to-digital conversion Resolution (bits) Sampling rate (Hz)

4 Analog EEG

5 Structure of digital EEG data A matrix consisting of Columns #: Number of channels Rows #: sampling rate (Hz) * time (msec)

6 Aliasing signal In order to reliably digitize a signal of a given frequency, F, the signal must be sampled at a rate of more than two times (Nyquist theorem) False representation of the source signal occurs when a signal of a certain frequency is sampled too slowly to resolve it frequency content aliasing (i.e., false name) ACNS guideline (2006) Acquisition of EEG data onto a digital storage medium should occur at a minimum sampling rate three times the high-frequency filter setting (e.g. 200 Hz for 70 Hz high frequency filter) Digitization should use a resolution of at least 11 bits per sample be able to resolve EEG down to 0.5 uv

7 Advantage of DEEG montage reformatting, virtual reference Flexible setting of gain, filter, and time base Easy measurement for amplitude and duration Convenient data storage and retrieval, networking Voltage topography Power spectrum and map Long-term monitoring by QEEG Spike and seizure detection Source localization: dipole, current distribution

8 Referential recording Digital EEG machine use the principle of referential recording (cf. analog EEG using bipolar recording) a separate electrode (called reference) is used as the second input into the differential amplifier for each channel (Fp1-ref, F7-ref, T7-ref, etc) Allow users to convert montage simply by subtracting or adding channels appropriately (montage reformatting) Bipolar Fp1-F7 = (Fp1-ref) (F7-ref) virtual reference: linked ears (A1A2), common average reference (AVR)

9 Digital filtering Digital filter do not cause any delay in the signal EEG removed by analog filters during acquisition cannot be recovered Typical filter sequence for routine EEG review 1) use broad-pass analog filter setting for acquisition (scalep Hz, intracranial Hz) 2) Review EEG initially using broad-pass digital filter ( Hz) 3) apply notch filter (60 Hz) if line noise present 4) brief apply HFF as needed during clinical events 5) Apply continuous low and high filter only for extremely agitated or uncooperative patients

10 Sources of EEG Current sources Synchronous excitation of brain tissue Volume conduction Current flows trough brain tissues Measurement by electrode Potential difference

11 Characteristics of EEG signal Intracellular current by neuronal interaction Synaptic potential of neurons (pyramidal cell, glial cell, interneurons) voltage change over time: time domain Oscillation of neuronal assembly and their networks (short and long association fibers) oscillatory characteristics: frequency domain Multichannel recording from multiple brain regions: spatial domain

12 Computational analysis Quantification Feature extraction Prediction Classification Noise reduction

13 Common methods of EEG analysis Time series analysis Linear analysis: correlation, prediction Nonlinear analysis: dimension, entropy, nonlinear prediction Frequency analysis Power spectral density Time-frequency analysis Spatial analysis Topographic map: voltage, power spectrum Functional connectivity: coherence, synchrony Source localization: current source, oscillatory source

14 Application of EEG analysis Quantitative comparison of functional brain state Comparison between patient and healthy control Correlation between clinical disease stage and EEG features Effect of CNS acting drugs, Depth of anesthesia Classification or characterization of CNS drugs Continuous EEG monitoring Spike detection, Seizure detection and prediction Topographic mapping and source localization Interictal, ictal activity Specific ERP components EEG modulation by internal or external stimulation Brain-computer interface (BCI)

15 EEG localization principles A-B A-E B-C B-E C-D C-E D-E D-E Negative Positive A>B>D>E>C Bipolar montage Referential montage

16 Radial vs. Tangential sources Dipole J Clin Neurophysiol 2007

17 Bipolar - Longitudinal, Transverse chain Referential Midline, Contralateral, Common average JKSCN 2003

18 Potential distribution (voltage topography) Regardless of point of reference, the features of the terrain do not change (reference independent) Shape of isopotential contour lines Voltage gradients (voltage slopes) Topographic distribution of negative and positive peak Different voltage distribution indicates different source in the brain

19 Assuming source from voltage topography 1) find two voltage field maxima (i.e., negative and positive) 2) draw a three-dimensional line between two maxima identify the orientation of the field 3) amplitude and gradient of the field maxima determines the source location depth of the source 4) the source should be proportionately closer to the field maxima of greater amplitude

20 Methods for source localization Model-independent Voltage topographic map Current source derivation (Laplacian) Model-dependent Cortical potential image Source localization Discrete method Distributed method

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23 Frequency analysis Delta, theta, alpha, beta, gamma Frequency and brain state Sleep and wakefulness Level of consciousness Frequency and cognition Brain maturation Intelligence Higher cortical function

24 Frequency band Delta (0.5-4 Hz) Theta (4-8 Hz) Alpha (8-13 Hz) Beta (13-30 Hz) Gamma (30-50 Hz) Infraslow (<0.5Hz) Ultrafast (ripple, HFO) Function Signal detection, Decision making Selective attention, Orienting, working memory Sensory, Movement, Memory Movement, Response inhibition Early sensory, Attention, Perceptual switching Task Execution, Slow cyclic modulation during sleep, vigilance states, ictal shift Epileptogenesis, Memory consolidation

25 Power spectral analysis Fourier transform Time domain Frequency domain A Fourier series decomposes periodic signals into the sum of a (possibly infinite) set of simple oscillating functions, namely sines and cosines

26 Measuring functional connectivity Spectral Coherence in frequency domain the frequency-indexed correlation coefficient estimating the linear relationship between two time series Phase Synchronization direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two time series Variability of phase difference

27 Take home messages To detect more abnormalities, use multiple montages Flexible use paper speed, gain and filter (BUT judicious!) use quantitative measures such as amplitude and duration Frequency analysis is frequently useful Consider topographic distribution of abnormalities and use topographic map function

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