1. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 1 EEG Brain signal measurement and analysis 414BMT Dr Ali Saad, College of Applied medical sciences/ Department of biomedical technology King Saud University

2. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 2 Brain Structure cortex (grey matter) white matter cerebellum thallamus brainstem

3. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 3 Brain Function brainstem: stalk of brain, along which nerve fibers send information between spinal chord and higher brain structures. thalamus: relay station, integrates sensory information berfore it is sent to cortex (with the exception of smell). cerebellum: fine muscle movement control. white matter: contains fibers which interconnect thalamus and cortex (mostly myelinated axons, 1 cm 3 volume may contain 10 7 fibers). grey matter (cortex): 2-3 mm thick, total surface area of 1600 cm 2, contains about 10 10 neurons. Each neuron has about 10 3 to 10 5 synapses. Cortex is responsible for our conscious experience.

4. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 4 Electroencephalogram (EEG) Originates in cortical neurons, current due to EPSP and IPSP’s produce voltage drops across ECF which lead to scalp potentials (EEG). v + _ dendrites axon current lines {

5. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 5 Unipolar EEG Recording +_+_ + _ active site reference site

6. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 6 Bipolar EEG Recording +_+_ + _ record difference between two active sites

7. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 7 Ten-Twenty Electrode System

8. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 8 Normal EEG Rhythms Alpha: 8-13 Hz Beta: 14-30 Hz Theta: 4-7 hz Delta: <3.5 1 sec

9. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 9 EEG Rhythms (cont.) n Alpha (8-13 Hz): occur in quiet, restful mental state, most intense over the occipital region, especially when the eyes are closed. n Beta (14-30 Hz): recorded from parietal and frontal regions, two types: n Beta I: disappear during intense mental activity leaving low frequency wave. n Beta II: occur during intense mental activity. n Theta (4-7 Hz): parietal and temporal regions in children. n Delta (<3.5): deep sleep.

10. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 10 Clinical EEG (Aminoff)

11. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 11 n A random variable, x, assumes values on the real line. n The values it assumes are governed by a probability density function, p(x). n The probability that x lies in the interval, (x 0, x 0 +  x) is: n Note that Introduction to Random Variables

12. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 12 Introduction to Random Variables (cont.) n The average or expected value of x is: n The second moment of x is: n The variance of x is:

13. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 13 Evoked Potentials +_+_ + _ strobe light flashes, elicits evoked potential +EEG: + EP EEG = “single trial”

14. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 14 Evoked Potential Estimation n Signal Averaging: most common method n Wiener Filtering: minimizes MSE n Matched Filtering: maximizes SNR (assumes signal known) n Weighted Averaging: Maximizes SNR (signal need not be known) Seeks to determine the EP given noisy measurements:

15. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 15 Signal Averaging n Apply M stimuli, and record resulting M responses. n Align M responses to form an ensemble with each response EP aligned in time. n Average across all responses in the ensemble to get the EP estimate n Assume each response, is sampled N times using an A/D converter:

16. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 16 Signal Averaging (cont.)

17. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 17 Analysis of Signal Averaging

18. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 18 Analysis of Signal Averaging (cont.) all signal components are identical:

19. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 19 Signal Power Estimates Ensemble average signal power estimate: Single trial signal power estimate: These estimates are based on the definition of average power for continuous-time signals:

20. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 20 Noise Power The noise, z i, is an example of a random process. We must use statistical expectation to determine noise power. Single trial noise power is equal for each trial: Noise components are uncorrelated across trials:

21. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 21 Noise Power (cont.) Ensemble average noise power: due to the uncorrelated assumption noise power in average is reduced by a factor of M

22. Dr. Ali Saad modified from Dr. Carlos Davila Southe. metho univ 22 Signal to Noise Ratio (SNR) of Single Trial Signal to Noise Ratio (SNR) of Ensemble Average: SNR of ensemble average is M times the SNR of each single trial.