A frequency recognition method based on multitaper spectral analysis and SNR estimation for SSVEP-based brain-computer interface

Over the past several years, steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have attracted wide attention in the field of BCIs research due to high information transfer rate, little user training, and applicability to the majority. In conventional recognition methods for training-free SSVEP-based BCIs, the energy difference between the frequencies of electroencephalogram (EEG) background noise is usually ignored, therefore, there is a significant variance among the recognition accuracy of different stimulus frequencies. In order to improve the performance of training-free SSVEP-based BCIs system and balance the accuracy of recognition between different stimulus frequencies, a recognition method based on multitaper spectral analysis and signal-to-noise ratio estimation (MTSA-SNR) is proposed in this paper. A 40-class SSVEP public benchmark SSVEP dataset recorded from 35 subjects was used to evaluate the performance of the proposed method. Under the condition of 2.25s data length, the accuracy of the three methods were 81.1% (MTSA-SNR), 74.5% (canonical correlation analysis, CCA) and 73.4% (multivariate synchronization index, MSI), and the corresponding ITRs were 101 bits/min (MTSA-SNR), 89 bits/min (CCA), 87 bits/min (MSI). In the low frequency range (8–9.8Hz), the average recognition accuracy of the three methods is 82.9% (MTSA-SNR), 82.0% (CCA), 83.3% (MSI). The average accuracy of the three methods was 78.6% (MTSA-SNR), 64.9% (CCA) and 61.8% (MSI) in the high frequency range (14–15.8Hz). According to the results, the proposed method can effectively improve the performance of training-free SSVEP-based BCI system, and balance the recognition accuracy between different stimulation frequencies.