1. Discussion Section: Review, Viirre Lecture Adrienne Moore 1-23-08

2. Your braiin (don’t worry about the subcortical structures for now)

3. EEG Diifferent frequency bands roughly correspond to diifferent “states”: delta, theta, alpha, beta, gamma mu (Piineda lecture) iis iin the alpha band, but has a diifferent source EEG iis noniinvasiive and has very good temporal resolutiion (but poor spatiial resolutiion) voltage time

4. EEG recording Electrodes: record signal  Eye electrodes -- EOG  High density array** Signal Amplifiers: increase signal  Active electrodes** Filters: select frequency band, exclude some noise from signal Computers: present stimuli, collect data for analysis ** “Improved, low-noise EEG recording systems “

5. EEG analysis 2 options -- Temporal domain :  Evoked Potential, ERP  Always averaged Frequency domain (spectral analysis) :  ERSP, power (Pineda’s approach)  Either averaged or “single- shot”  ICA makes single shot possible

6. Independent Component Analysis (ICA) Usefulness: Unmixes signals Makes “single shot” analysis possible Allows you to better estimate location of dipole sources within the brain Allows you to remove certain sorts of noise from EEG signal (eye movement, muscle movement)

7. ICA unmixing: how it works

8. “Scientific Learning Corp” example -- (ERPs) Auditory Training affects the brain:  Software to improve: visual attention, motor skills, and hand-eye coordination; listening and sequencing skills; sustained auditory attention and auditory discrimination  Applied it to language delayed/impaired kids Evidence that it works:  Language delayed/impaired kids develop clear P1/P2 distinction during training  After training language delayed kids scored well above the mean

9. Viirre’s research, 1st example: Error Detection (ERSP & ICA) 1. devise a task where people will make errors (navigation) 2. compare electrical activity of Correct vs Incorrect responses (differ around 10 Hz) 3. use real-time feedback of Correct vs Incorrect information to train people on cognitive tasks

10. Viirre’s 2 nd example: Speech Recognition (ERSP & ICA) 1. Look for brain signals specific to word meaning – “to” vs “too” 2. Use this to create “a computer that knows what I’m thinking, what my intentions are” 3. Create a database and train the system on a population ** Currently able to distinguish “to” vs “too” with greater than 85% accuracy