Abstract
Multifocal steady-state visual evoked potentials (mfSSVEPs) have been successfully applied to assess visual field loss in glaucoma. However, the potential of mfSSVEPs for command control has not been fully explored yet. It is significant to detect single-trial mfSSVEPs and establish a brain-computer interface (BCI) system. This study designed a stimulating paradigm that contains 32 targets, with each target composed of five fan-shaped flickers in a circle. The five flickers were modulated by five frequencies and formed a five-bit binary encoding system through controlling the ON/OFF state of each flicker. Twelve subjects participated in an offline and an online experiments. Inter-task-related component analysis (iTRCA) combined with a probabilistic model was proposed for target recognition. Notably, the training data needed for calibration corresponded to only six out of the 32 targets. It was found that the increasing number of flickers showed a negative impact on the mfSSVEP signal. The accuracy reached 80.9% ± 11.7% on average with a peak of 95.3% by iTRCA, which was significantly higher than that by a traditional method. The results indicate that the proposed stimulation and algorithm are effective for encoding and decoding BCI commands. Therefore, the mfSSVEP-based BCI enables the augmentation of the BCI instruction set without any burden of collecting extra training data.
Highlights
A brain-computer interface (BCI) could measure the brain signal of users and communicate with the external devices, which can help people with motor disabilities to improve the life quality [1], [2]
The state visual evoked potentials (SSVEPs) induced by repetitive stimulus at frequency above 6 Hz [10] were widely used in reactive BCIs to construct a large instruction set
Taking above issues into account, this study developed a novel inter-task-related component analysis algorithm by incorporating the interclass correlation between EEGs evoked by a single flicker and all flickers into the optimizing process of the spatial filter
Summary
A brain-computer interface (BCI) could measure the brain signal of users and communicate with the external devices, which can help people with motor disabilities to improve the life quality [1], [2]. Mir. most welcomed brain signal for BCIs. Event-related potentials (ERPs) [5], [6], steady-state visual evoked potentials (SSVEPs) [7] and sensory motor rhythms (SMRs) [8], [9] are exemplary EEG features for BCI development. Event-related potentials (ERPs) [5], [6], steady-state visual evoked potentials (SSVEPs) [7] and sensory motor rhythms (SMRs) [8], [9] are exemplary EEG features for BCI development Among these features, the SSVEPs induced by repetitive stimulus at frequency above 6 Hz [10] were widely used in reactive BCIs to construct a large instruction set. As a rhythmic signal that contains the spectral components at the fundamental and harmonic stimulating frequencies, the SSVEPs possess excellent stability and high signal-to-noise ratios (SNRs), becoming one of the most efficient EEG features to conduct cognitive research [11], [12] or realize a high-speed
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