Frequency detection for SSVEP-based BCI using deep canonical correlation analysis
| Autor: | Bonkon Koo, Hanh Vu, Seungjin Choi |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry Speech recognition Feature extraction Pattern recognition 02 engineering and technology Electroencephalography 03 medical and health sciences 0302 clinical medicine Signal-to-noise ratio Feature (computer vision) 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence Evoked potential Canonical correlation business 030217 neurology & neurosurgery Brain–computer interface |
| Zdroj: | SMC |
| Popis: | Canonical correlation analysis (CCA) has been successfully used for extracting frequency components of steady-state visual evoked potential (SSVEP) in electroencephalography (EEG). Recently, a few efforts on CCA-based SSVEP methods have been made to demonstrate the benefits for brain computer interface (BCI). Most of these methods are limited to linear CCA. In this paper consider a deep extension of CCA where input data are processed through multiple layers before their correlations are computed. To our best knowledge, it is the first time to apply deep CCA (DCCA) to the task of frequency component extraction in SSVEP. Our empirical study demonstrates that DCCA extracts more robust feature, which has significantly higher signal to noise ratio (SNR) compared to those of CCA, and it results in better performance in classification with the averaged accuracy of 92%. |
| Databáze: | OpenAIRE |
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