IPSIHAND AN EEG BASED BRAIN COMPUTER INTERFACE FOR MOTOR REHABILITATION

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

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

RECORDING TECHNIQUES Vs.

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

SCREENING DATA

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

ALTERNATIVE ADAPTATION TECHNIQUES Least Mean Squares Linear Regression

PERFORMANCE RESULTS Actual Hand Movement Imagined Hand Movement Left vs. Right

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