Inter- and Intra-subject Template-Based Multivariate Synchronization Index Using an Adaptive Threshold for SSVEP-Based BCIs

Abstract

The steady-state visually evoked potential (SSVEP) has been widely used in brain-computer interfaces (BCIs). Many studies have proved that the Multivariate synchronization index (MSI) is an efficient method for recognizing the frequency components in SSVEP-based BCIs. Despite its success, the recognition accuracy has not been satisfactory because the simplified pre-constructed sine-cosine waves lack abundant features from the real electroencephalogram (EEG) data. Recent advances in addressing this issue have achieved a significant improvement in recognition accuracy by using individual calibration data. In this study, a new extension based on inter-and intra-subject template signals is introduced to improve the performance of the standard MSI method. Through template transfer, inter-subject similarity and variability are employed to enhance the robustness of SSVEP recognition. Additionally, most existed methods for SSVEP recognition utilize a fixed time window (TW) to perform frequency domain analysis, which limits the information transfer rate (ITR) of BCIs. For addressing this problem, a novel adaptive threshold strategy is integrated into the extension of MSI, which uses a dynamic window to extract the temporal features of SSVEPs and recognizes the stimulus frequency based on a pre-set threshold. The pre-set threshold contributes to obtaining an appropriate and shorter signal length for frequency recognition and filtering ignored-invalid trials. The proposed method is evaluated on a 12-class SSVEP dataset recorded from 10 subjects, and the result shows that this achieves higher recognition accuracy and information transfer rate when compared with the CCA, MSI, Multi-set CCA, and Individual Template-based CCA. This paper demonstrates that the proposed method is a promising approach for developing high-speed BCIs.

Cross-Subject Assistance: Inter- and Intra-Subject Maximal Correlation for Enhancing the Performance of SSVEP-Based BCIs

“…Perhaps the reason is that they can indeed deploy overt attention to the task-relevant stimuli and focus on them [42], which highlights the importance of training in advance. On the other hand, when it is not clear who has the experience, one alternative way is to select subjects with higher accuracy as the ideal transferred subjects (i.e., the experienced group), which has been verified in our previous study [43].…”

Control of a Robotic Arm With an Optimized Common Template-Based CCA Method for SSVEP-Based BCI

“…How to realize the information interaction between people and external equipment simply and conveniently has always been the goal of human beings, and the brain-computer interface (BCI) provides this possibility. Specifically, BCI is a control and communication system that can recognize or convert brain activity information into control commands of external devices (Wang et al, 2020 ). Compared to ordinary input interactive devices, the BCI input is the brain signals recorded by electrodes on the head, and the output applications can be controlled directly from the brain, such as robotic arms (Aljalal et al, 2020 ; Zhu et al, 2020 ), wheelchairs (Li et al, 2016 ; Deng et al, 2019 ; Bonci et al, 2021 ), character speller systems (Rezeika et al, 2018 ; Podmore et al, 2019 ), and other devices (Gao et al, 2019 ).…”

“…The filter bank CCA (FBCCA) method enhances SSVEP detection by decomposing the signal into sub-band and further using the harmonic information in them (Chen et al, 2015 ). Furthermore, there are approaches such as individual template-based CCA (IT-CCA) and transfer template-based (tt-CCA) that used real EEG data to construct new signal templates for frequency identification (Nakanishi et al, 2015 ; Yuan et al, 2015 ; Wang et al, 2020 ). Many comparative studies showed that the extension methods of CCA with supervised (such as IT-CCA) have better performance of recognition accuracies and information transfer rate (ITR) than training-free (such as CCA and FBCCA) (Nakanishi et al, 2015 ; Zerafa et al, 2018 ; Saidi et al, 2019 ).…”

cVEP Training Data Validation—Towards Optimal Training Set Composition from Multi-Day Data

“…Wong et al presented a subject transfer-based CCA method to combine the information between the subjects and within the subject, reaching an average ITR of 198.18 bits/min [ 13 ]. Wang et al proposed an inter- and intra-subject template-based multivariate synchronization index with an adaptive threshold for a 12-class SSVEP-based BCI dataset [ 14 ]. The results from 10 subjects showed an average accuracy of 99.2% for the new method compared to the standard CCA, which reached 93.6%.…”