SSVEP enhancement based on Canonical Correlation Analysis to improve BCI performances

[jinglescode]

Paper

Link: https://hal.archives-ouvertes.fr/hal-01352059/document
Year: 2013

Summary

• this investigate CCA as a signal enhancement method and not as a feature extraction method
• make use of the ability of CCA to handle multichannel EEG and find the space in which EEG samples correlate the most with the stimuli
• CCA yields effective weights (spacial filters) with relatively small training sets

Methods

• light emitting diodes (LED) are used to flash stimuli at 13 Hz, 17 Hz, and 21 Hz
• subject gazes at the flash stimuli for a period of 5 seconds followed by a 3 second break
• EEG and find the space in which EEG samples correlate the most with the stimuli. To enhance SSVEP features, EEG samples are projected into that space. Power spectral density analysis (PSDA) is then used for feature extraction. To evaluate the impact that CCA based signal enhancement has on classification performances, an SVM classifier is used

Results