Eeg examples

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Eeg examples

  1. 1. EEG recordings in man Examples of records and results of analysis made by Braintune (St. Petersbuurg) hardware/software.
  2. 2. Nyquist Theorem • The highest frequency which can be accurately represented is one-half of the sampling rate. • The sampling rate here is below the Nyquist frequency, so the result of sampling is nothing like the input:aliasing. • For practical purposes the sampling rate should be 10 higher than the highest frequency in the signal. •
  3. 3. EEG is a difference in potential between two electrodes • If two electrodes are “active”, it is called “bipolar” recording. • If one electrode is “silent”, it is called “monopolar” recording. The reference sites: ear lobe, mastoid, nose.
  4. 4. Bipolar vs. monopolar recordings • Monopolar recording is used in research, because it enables the researcher to localize the event of interest. • Bipolar recording is used in BF, because it reduces shared artifacts. Electrodes should be placed on the sites with the strongest gradients of the potentials under training.
  5. 5. EMG and eye blink artifacts in EEG • These types of artifacts can be detected by visual inspection. • Eye blinks can be excluded from data analysis. • EMG should be taken into account during spectral analysis.
  6. 6. EEG recorded at Cz • EEG is a mixture of waves at different frequencies and amplitudes.
  7. 7. EEG recorded at T5 • At each time interval several sine-waves at different frequencies may be present in the signal.
  8. 8. Quality control of EEG recording • 1) EEG amplifies must be calibrated with daily checks • 2) acquisition parameters must be checked daily and keep the same • 3) the same procedures must be employed in all individuals • 4) all artifacts must be eliminated or taken into account prior to spectral analysis.
  9. 9. EMG Artifact • EMG artifact starts as low as 12 Hz and ranges to 300 Hz. Most of the spectrum lies between 30-150 Hz. • Sites F3, F4, T3, T4, P3, P4 can pick up EMG the massester and temporalis muscles. • Posterior electrodes can pick up EMG from occipitalis, trapezius and supraspinal muscles. • To avoid this type of artifact one can relax or position the head properly or change slightly the position of electrode. • Fz, Cz, Pz can give a relatively pure EEG signal.
  10. 10. EKG artifact • ECG artifacts occur from the electrodes that pick up activity from underlying pulsating blood vessels in the scalp. • EKG artifact gets more prevalent with aging.
  11. 11. Ocular, blinks and electroretinal activity • Eye movement and blinks artifacts occur in the delta range 0-4 Hz and occur over the anterior part of the scalp.
  12. 12. The noise from the standard AC electrical line current • This noise can be diminished by the proper grounding of the equipment (both computer and amplifies). • It could be also eliminated by a so called notch filter which selectively removes 50 (60 for the US) Hz activity from the signal. • This noise could be attenuated by obtaining good contact of electrodes with the scalp. The electrode impedance less than 10 kOhms is desirable.
  13. 13. EEG recording in man • Eyes opened condition. • Examples of different waves.
  14. 14. Reviewing EEG • EEG is characterized by: • 1) voltage • 2) frequency (is used for BF) • 3) spatial location (is used for BF) • 4) inter-hemispheric symmetries • 5) reactivity (reaction to state change) • 6) character of waveform occurrence (random, serial, continuous) • 6) morphology of transient events
  15. 15. Reviewing EEG: voltage • Amplitude is the voltage in microvolts measured from the peak of the wave to the trough of the wave. Varies from 10 mcV to 100 mcV with average around 20-50 mcV. •
  16. 16. Reviewing EEG: frequency • Spectrums reflect the amount of energy in a certain frequency range of EEG. • Term monorhythmic means that a particular portion of EEG shows a rhythmic component in a singular frequency. • Term polyrhythmic means that several rhythmic frequencies are present in EEG. • The presence of large-amplitude delta- activity may indicate infarct or other lesion.
  17. 17. Reviewing EEG: frequency • Slow (0-4 Hz) and high (more 20 Hz) frequency bands of EEG may pick up artifacts, such as eye movements and muscle activity, and therefore should be evaluated with caution. • Despite the use of artifact rejection algorithms, the failure to accurately distinguish true physiological rhythmicity from the artifacts is a serious shortcoming of current software systems and requires the expert assessment.
  18. 18. Reviewing EEG: transient events • A transient is an isolated form or feature that stands out from the background activity. • It is called a spike if it has the duration less than 70 msec. • It is called a sharp wave if it has the duration between 70 and 200 msec. • The presence of large amplitude spikes and waves may indicate the presence of epilepsy.
  19. 19. Maps of EEG spectrums in standards bands • Eyes opened condition.
  20. 20. EEG recording in man • Eyes closed condition. • Enhancement of alpha waves.
  21. 21. Maps of EEG spectrums • Eyes closed condition
  22. 22. EEG spectrums • Three conditions (EC, R, M) are compared to Eyes Opened condition. • Two peaks (in theta and alpha band) with different scalp distribution are observed. • Reading and math produce big (but different) changes in alpha band and small changes in theta and beta bands. • Note that alpha activities are different for all four conditions both in distribution and frequency.
  23. 23. EEG spectrums in individual bands • Regular theta - idling rhythm • Irregular theta - working activity • Reading and math produce alpha rhythms that are different in frequency and location.
  24. 24. EEG as a sequence of micro- states • EEG consists of series of short-lasting quasi- stationary epochs corresponding to what Lehmann et al. (1987) have called brain functional micro-states. • EEG reflects the changes in the state of neuronal networks rather than specific aspects of information processing.
  25. 25. Normal distribution • When many independent random factors act in an additive manner to create variability, data will follow a bell-shaped distribution called the Gaussian distribution. This distribution is also called a Normal distribution. • Although no data follows that mathematical ideal, many kinds of data follow a distribution that is approximately Gaussian
  26. 26. Dysfunction as a deviation from normal distribution • If we measure some parameter in the population with some brain dysfunction, then this parameter must has a different, not Gaussian distribution. • There are statistical tests that measure this difference.
  27. 27. Bimodal distribution in ADHD? • Clinicians who diagnose this disorder have been criticized for merely taking a percentage of the normal population who have the most evidence of inattention and continuous activity and labeling them as having a disease. In fact, it is unclear whether the signs of ADHD represent a bimodal distribution in the population or one end of a continuum of characteristics. This is not unique to ADHD as other medical diagnoses, such as essential hypertension and hyperlipidemia, are continuous in the general population, yet the utility of diagnosis and treatment have been proven. Nevertheless, related problems of diagnosis include differentiating this entity from other behavioral problems and determining the appropriate boundary between the normal population and those with ADHD.
  28. 28. Life span normative EEG database (LNDB) • There are at least four eyes-closed LNDB: • 1) E. Roy John et al. (1977) • 2) Frank Duffy et al. (1994) • 3) Robert Thatcher et al. (1987) • 4) Barry Sterman et al. (199?)
  29. 29. Three goals of LNDB • 1) to assess the neurological basis for the patient’s complains (the issue of organicity) • 2) to identify the weakness of electrophysiological organization of the brain (the issue of neurotherapy design) • 3) to evaluate the efficacy of treatment ( the issue of treatment evaluation) • Thatcher, 1999
  30. 30. Active and “passive” conditions for NDB • 1) Eyes opened and eyes closed conditions are often used in NDB, because of simplicity and relative uniformity of recording conditions. • 2) Active tasks depend of many uncontrolled factors, such as intensity of stimuli, the subject’s involvement, the distance from stimuli, etc. There are no standards for active conditions.

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