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http://hdl.handle.net/10012/830
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| Title: | Using Temporal Evidence and Fusion of Time-Frequency Features for Brain-Computer Interfacing |
| Authors: | Dharwarkar, Gireesh |
| Keywords: | Electrical & Computer Engineering
Brain-Computer Interfacing
Motor Imagery
EEG signal processing
Adaptive Autoregression
Wavelet
Kalman Filtering |
| Approved Date: | 2005 |
| Date Submitted: | 2005 |
| Abstract: | Brain-computer interfacing (BCI) is a new method of human-machine interaction. It involves the extraction of information from the electroencephalogram (EEG) through signal processing and pattern recognition. The technology has far reaching implications for those with severe physical disabilities and has the potential to enhance machine interaction for the rest of the population. In this work we investigate time-frequency analysis in motor-imagery BCI. We consider two methods for signal analysis: adaptive autoregressive models (AAR) and wavelet transform (WAV). There are three major contributions of this research to single-trial analysis in motor-imagery BCI. First, we improve classification of AAR features over a conventional method by applying a temporal evidence accumulation (TEA) framework. Second, we compare the performance of AAR and WAV under the TEA framework for three subjects and find that WAV outperforms AAR for two subjects. The subject for whom AAR outperforms WAV has the lowest overall signal-to-noise ratio in their BCI output, an indication that the AAR model is more robust than WAV for noisier signals. Lastly, we find empirical evidence of complimentary information between AAR and WAV and propose a fusion scheme that increases the mutual information between the BCI output and classes. |
| Department: | Electrical and Computer Engineering |
| Degree: | Master of Applied Science |
| URI: | http://hdl.handle.net/10012/830 |
| Appears in Collections: | Faculty of Engineering Theses and Dissertations
Electronic Theses and Dissertations (UW)
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