Multisymbol Time Division Coding for High-Frequency Steady-State Visual Evoked Potential-Based Brain-Computer Interface

Abstract

The optimization of coding stimulus is a cruial factor in the study of steady-state visual evoked potential (SSVEP)-based brain-computer interface(BCI).This study proposed an encoding approach named Multi-Symbol Time Division Coding (MSTDC). This approach is based on a protocol of maximizing the distance between neural responses, which aims to encode stimulation systems implementing any number of targets with finite stimulations of different frequencies and phases. Firstly, this study designed an SSVEP-based BCI system containing forty targets with this approach. The stimulation encoding of this system was achieved with four temporal-divided stimuli that adopt the same frequency of 30Hz and different phases. During the online experiments of twelve subjects, this system achieved an average accuracy of 96.77±2.47% and an average information transfer rate (ITR) of 119.05±6.11 bits/min. This study also devised an SSVEP-based BCI system containing 72 targets and proposed a Template Splicing task-related component analysis (TRCA) algorithm that utilized the dataset of the previous system containing forty targets as the training dataset. The subjects acquired an average accuracy of 86.23±7.75% and an average ITR of 95.68±14.19 bits/min. It can be inferred that MSTDC can encode multiple targets with limited frequencies and phases of stimuli. Meanwhile, this protocol can be effortlessly expanded into other systems and sufficiently reduce the cost of collecting training data. This study provides a feasible technique for obtaining a comfortable SSVEP-based BCI with multiple targets while maintaining high information transfer rate.

Optimizing Stimulus Frequency Ranges for Building a High-Rate High Frequency SSVEP-BCI

“…In contrast to low-and medium-frequency stimuli, the high-frequency stimulation is considered less annoying. Please note that high-frequency SSVEP is more suitable for building a comfortable system [30], [48], [49]. According to the comfort questionnaire results (see Fig.…”

Visual tracking brain-computer interface

“…However, exploring these differences could potentially provide a path to enhance BCI performance. 38 From a different perspective, non-equivalent stimulus sequences, such as sinusoidal sequences with different frequencies, may hold greater potential in certain scenarios with preferences for specific directions. In this study, we employed task-related component analysis (TRCA) as the template matching method.…”

Enhancing Detection of Multi-Frequency-Modulated SSVEP Using Phase Difference Constrained Canonical Correlation Analysis

“…The pdCCA + is a combination of the CCA and the pdCCA. 13)-( 15) may be written as ( 16)- (18), in which r k,n b and ρ k,n b denote the correlation coefficients for the n b -th sub-band data through the CCA and the pdCCA, respectively.…”

“…In this case, a subject's single-trial SSVEP is encoded using more than one stimulus frequency. For example, the multiple frequencies are used to drive one flicker in different timeslots [14]- [18], drive one flicker at the same time [19], [20], and drive different flickers at the same time [21], [22], respectively, as illustrated in Fig. 1.…”