Abstract
Among various methods for frequency recognition of the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) study, a task-related component analysis (TRCA), which extracts discriminative spatial filters for classifying electroencephalogram (EEG) signals, has gathered much interest. The TRCA-based SSVEP method yields lower computational cost and higher classification performance compared to existing SSVEP methods. In spite of its utility, the TRCA-based SSVEP method still suffers from the degradation of the frequency recognition rate in cases where EEG signals with a short length window are used. To address this issue, here, we propose an improved strategy for decoding SSVEPs, which is insensitive to a window length by carrying out two-step TRCA. The proposed method reuses the spatial filters corresponding to target frequencies generated by the TRCA. Followingly, the proposed method accentuates features for target frequencies by correlating individual template and test data. For the evaluation of the performance of the proposed method, we used a benchmark dataset with 35 subjects and confirmed significantly improved performance comparing with other existing SSVEP methods. These results imply the suitability as an efficient frequency recognition strategy for SSVEP-based BCI applications.
Highlights
The brain-computer interface (BCI) provides a bidirectional system between the human brain and external devices by decoding electrical brain waves measured in specific environments
To evaluate the proposed two-step TRCA (TSTRCA) method compared to the existing state visual evoked potential (SSVEP) frequency recognition methods such as canonical correlation analysis (CCA), extended CCA (ExtCCA), and task-related component analysis (TRCA), we used the classification accuracy and the information transfer rate (ITR) as two metrics to measure the frequency detection performance
We presented a novel frequency recognition method for SSVEP-based BCI based on the TRCA method
Summary
The brain-computer interface (BCI) provides a bidirectional system between the human brain and external devices by decoding electrical brain waves measured in specific environments. In years past, real-time BCI applications such as brain-controlled vehicles (BCVs) [4] and brain-controlled wheelchairs (BCWs) [5] that can be facilitated in daily life have received enormous attention To control these applications, in the BCI study, EEG signals can be divided into different forms depending on the purpose of use, its type, and so on. In the BCI study, EEG signals can be divided into different forms depending on the purpose of use, its type, and so on Among those forms, steady-state visual evoked potential (SSVEP) has attracted much attention due to the high communication rate, classification accuracy, and high signal-to-noise ratio (SNR) [6,7]. The number of SSVEP-based real-time BCI applications have resulted in remarkable achievements [4,5,8,9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
