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On Line Single Trial Detection of Index Finger Flexions from Spatiotemporal EEG

University of British Columbia

A Brain Computer Interface (BCI) detects commands directly from the operator's brain and provides an output which can be easily interpreted by a computer. Much of the research to date has focused on differentiating between several possible commands rather than between control signals and the idle state. For most practical applications, it is essential that the operator is able to rest without issuing unintended commands. Mason proposes an Asynchronous Signal Detector (ASD) which allows existing BCI techniques to function outside the laboratory. The ASD continuously monitors the electroencephalograph (EEG) identifying segments which contain commands and acts as a switch which selectively relays EEG to a Control Signal Classifier (CSC). Existing BCI methods may be used for the CSC in order to determine which command the operator intended to issue. Alternatively, the ASD can function as a stand alone system capable of recognizing a single control signal. The goal of this research was to implement an ASD capable of identifying Voluntary Movement Related Potentials (VMRPs) from a continuous sampling of surface electrodes spatially distributed over the motor areas of the brain. Features were extracted from EEG components below 4 Hz as in Mason's Low Frequency ASD (LF-ASD) and classified using Learning Vector Quantization (LVQ). A revised version of Mason's ASD was implemented as an on line system. Two able bodied subjects each participated in three sessions as a preliminary evaluation of the ASD in detecting right handed index finger flexions. Subject training was also briefly investigated by providing the participants with feedback one second after movements were detected. Training appeared to take place rapidly with the percentage of detected movements for both subjects increasing from 24.0% in the first session to 45.3% and 49.3% respectively during the second attempt. During the three sessions for each subject 90.4% up to 99.2% of the idle E EG collected was correctly classified. Further research is required to evaluate the ASD as a generic VMRP detector and to test the method with participants who have little or no motor control.

Thesis/Dissertation

application/pdf

2009-05-19

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http://hdl.handle.net/2429/7954

Electrical and Computer Engineering

University of British Columbia

UBCV

DSpace