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.
Highlights
The Brain-Computer Interfaces (BCIs) provide humans with a direct communication and control channel between human brains and external devices by utilizing brain signals produced along the cerebral cortex within the brain to directly control external devices without the aid of muscular movements (Dornhege et al, 2007; Faller et al, 2010)
Several specific brain activity patterns, including Slow Cortical Potentials (SCPs), P300 evoked potentials, Steady-State Visually Evoked Potentials (SSVEPs), Event-Related Desynchronization (ERD), and Synchronization (ERS), have been investigated extensively, as these have served as the source of stimulation signals for BCI control (Zhang et al, 2014b)
The SSVEP-BCIs rely on oscillatory responses occurring in the occipital and the occipito-parietal cortex that are elicited from a stimulus flickering at a specific frequency (Vu et al, 2016; Georgiadis et al, 2018)
Summary
The Brain-Computer Interfaces (BCIs) provide humans with a direct communication and control channel between human brains and external devices by utilizing brain signals produced along the cerebral cortex within the brain to directly control external devices without the aid of muscular movements (Dornhege et al, 2007; Faller et al, 2010). People with disabilities, such as limb loss, spinal cord injury, and amyotrophic lateral sclerosis, can draw support from BCIs to assist with the activities involved in daily life. The SSVEP signals are the inherent response of the brain, and the SSVEP-based BCI systems required minimal to no training (Bin et al, 2009)
Sign-in/Register to view highlights & summary 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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
