1. IPSIHAND AN EEG BASED BRAIN COMPUTER INTERFACE FOR MOTOR REHABILITATION

2. MOTIVATION 900,000 individuals in US with severe difficulty grasping Causes: Stroke Traumatic Brain Injury Spinal Cord Injury Neurodegenerative diseases

3. MOTIVATION 1.Restore hand control 2.Provide novel rehabilitation therapy

7. RECORDING TECHNIQUES Vs.

9. SCREENING PROCEDURE 2 Conditions: Left Hand Movement Rest Look for change in EEG signal between conditions

10. SCREENING DATA

11. CURRENT SIGNAL PROCESSING 1.Band-Pass Filtering 2.Spatial Filtering 1.Raw 2.Common Average 3.Bipolar – pick an electrode 3.Autoregressive Spectral Estimation 4.Feature Selection 5.Control Signal Normalization, Adaptation 0 mean, unit variance Adapted to buffer of previous data

12. ALTERNATIVE ADAPTATION TECHNIQUES Least Mean Squares Linear Regression

13. PERFORMANCE RESULTS Actual Hand Movement http://www.youtube.com/watch?v=4-7A5Q-kz-M&t=1m40s Imagined Hand Movement http://www.youtube.com/watch?v=4-7A5Q-kz-M&t=3m04s Left vs. Right http://www.youtube.com/watch?v=4-7A5Q-kz-M&t=4m34s

14. CHALLENGES, POSSIBLE PROJECTS Signal Processing (subject specific, non-stationary) Spatial Filter optimization Feature identification Adaptation Automation Hardware & Software Miniaturization – embedded platforms Power Consumption Comparison with alternative BCI software platforms (OpenViBE) Performance 2D, 3D control Latency reduction Accuracy gain Alternative actuators