Abstract
This study presents a novel waveform-coding method for multi-target steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs). Three periodic waveforms including square, sawtooth, and sinusoidal waves at various frequencies and initial phases were employed to elicit discriminable SSVEPs. A virtual keyboard was first designed using 36 visual stimuli modulated by the combinations of different frequencies, phases, and waveforms. With the virtual keyboard, 13 healthy participants performed offline and online BCI experiments with a cue-guided spelling task. The task-related component analysis (TRCA)-based algorithm was used to identify a target visual stimulus. The offline results showed that the visual stimuli tagged with different properties could accurately be identified by analyzing the elicited SSVEPs. Moreover, the online spelling task achieved promising performance with an averaged information transfer rate (ITR) of 62.6 ± 32.5 bits/min. This study validated the feasibility of implementing a multi-command SSVEP-based BCI using the hybrid waveform-, frequency- and phase-coding method. The proposed waveform-coding method provides a completely new channel for multi-target stimulus coding, expanding the research fields of an SSVEP-based BCI.
Highlights
Steady-state visual evoked potentials (SSVEPs) are the electroencephalographic (EEG) responses to flickering visual stimulation
The frequency components in SSVEPs are nearly stationary, and the stimulus frequency can be reliably recognized by analyzing SSVEPs in the frequency domain [3]
The waveform-coding method was introduced as a novel approach for designing a multi-command SSVEP-based brain–computer interfaces (BCIs) in this paper
Summary
Steady-state visual evoked potentials (SSVEPs) are the electroencephalographic (EEG) responses to flickering visual stimulation. In the human visual cortex, the activation of neurons synchronizes to the flickering of the visual stimuli, resulting in an SSVEP characterized by a sinusoidal-like waveform at the stimulus frequency and its harmonics [1], [2]. The frequency components in SSVEPs are nearly stationary, and the stimulus frequency can be reliably recognized by analyzing SSVEPs in the frequency domain [3]. Due to the robust frequency characteristics of SSVEPs, the frequency tagging technique, which encodes multiple visual stimuli with different flickering frequencies, The associate editor coordinating the review of this manuscript and approving it for publication was Carmen C.
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