Abstract
In one type of brain-computer interface (BCI), users self-modulate brain activity as detected by electroencephalography (EEG). To infer user intent, EEG signals are classified by algorithms which typically use only one of the several types of information available in these signals. One such BCI uses slow cortical potential (SCP) measures to classify single trials. We complemented these measures with estimates of high-frequency (gamma-band) activity, which has been associated with attentional and intentional states. Using a simple linear classifier, we obtained significantly greater classification accuracy using both types of information from the same recording epochs compared to using SCPs alone.
Original language | English (US) |
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Pages (from-to) | 1052-1056 |
Number of pages | 5 |
Journal | IEEE Transactions on Biomedical Engineering |
Volume | 51 |
Issue number | 6 |
DOIs | |
State | Published - Jun 2004 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
Keywords
- Multitaper
- Spectral analysis