| Paper: | MLSP-P6.6 | ||
| Session: | Biomedical and Other Applications | ||
| Time: | Friday, May 19, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Biomedical Applications and Neural Engineering | ||
| Title: | Detection of Hand Extension Movements in the Context of a 3-State Asynchronous Brain Interface | ||
| Authors: | Ali Bashashati, Rabab K. Ward, Gary E. Birch, University of British Columbia, Canada | ||
| Abstract: | The low-frequency asynchronous switch design (LF-ASD) is a direct brain interface (BI) that detects the presence of a specific finger movement in the ongoing EEG. Asynchronous interfaces have the advantage of being operational at all times and not only at specific system-defined periods. In this paper, we present the design of a 3-state asynchronous BI for the detection of two different movements from the ongoing EEG. The proposed 3-state asynchronous BI detects right and left hand extensions. Using data collected from two able-bodied individuals, it is shown that the error characteristics of the new system in detecting the presence of movement are significantly better than the 2-state LF-ASD, with true positive rate increases of up to 22.4% for false positive rates in the 1-2% range. An average performance of 61.5% was achieved in differentiating between left and right hand movements. | ||
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