Periodic component analysis as a spatial filter for SSVEP-based brain-computer interface
| Autor: | M. Ramasubba Reddy, G R Kiran Kumar |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Computer science Interface (computing) 0206 medical engineering 02 engineering and technology Pattern Recognition Automated 03 medical and health sciences Young Adult 0302 clinical medicine Signal-to-noise ratio Component analysis Humans Brain–computer interface Statistical hypothesis testing Visual Cortex Brain Mapping Principal Component Analysis Spatial filter business.industry General Neuroscience Pattern recognition Electroencephalography 020601 biomedical engineering Electric Stimulation Noise Brain-Computer Interfaces Evoked Potentials Visual Artificial intelligence business 030217 neurology & neurosurgery Energy (signal processing) Algorithms Photic Stimulation |
| Zdroj: | Journal of neuroscience methods. 307 |
| ISSN: | 1872-678X |
| Popis: | Background Traditional spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost. New method In this paper, Periodic component analysis ( π CA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The π CA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates. Results Data from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results. Comparison with existing methods The experimental results show that π CA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions. Conclusions The results demonstrate that π CA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence π CA is a reliable and efficient alternative detection algorithm for SSVEP based brain–computer interface (BCI). |
| Databáze: | OpenAIRE |
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