Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10368440 | Biomedical Signal Processing and Control | 2013 | 7 Pages |
Abstract
Brain computer interfaces (BCI) provide a new approach to human computer communication, where the control is realised via performing mental tasks such as motor imagery (MI). In this study, we investigate a novel method to automatically segment electroencephalographic (EEG) data within a trial and extract features accordingly in order to improve the performance of MI data classification techniques. A new local discriminant bases (LDB) algorithm using common spatial patterns (CSP) projection as transform function is proposed for automatic trial segmentation. CSP is also used for feature extraction following trial segmentation. This new technique also allows to obtain a more accurate picture of the most relevant temporal-spatial points in the EEG during the MI. The results are compared with other standard temporal segmentation techniques such as sliding window and LDB based on the local cosine transform (LCT).
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Javier Asensio-Cubero, John Q. Gan, Ramaswamy Palaniappan,