Abstract
Dimensionality reduction and feature selection is an important aspect of electroencephalography based event related potential detection systems such as brain computer interfaces. In our study, a predefined sequence of letters was presented to subjects in a Rapid Serial Visual Presentation (RSVP) paradigm. EEG data were collected and analyzed offline. A linear discriminant analysis (LDA) classifier was designed as the ERP (Event Related Potential) detector for its simplicity. Different dimensionality reduction and feature selection methods were applied and compared in a greedy wrapper framework. Experimental results showed that PCA with the first 10 principal components for each channel performed best and could be used in both online and offline systems.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 6329-6332 |
| Number of pages | 4 |
| Journal | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference |
| State | Published - 2010 |
| Externally published | Yes |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Biomedical Engineering
- Health Informatics
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