Abstract
The brain–computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired the electroencephalogram (EEG) data from a self-developed dedicated EEG device and the stimulation was arranged as a keyboard. The task-related component analysis (TRCA) spatial filter was modified (mTRCA) for target classification and showed significantly higher performance compared with the original TRCA in the offline analysis. In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. In addition, the temporal filtering process and the programs were optimized to facilitate the online DS operation. Notably, the online ITR reached 330.4 ± 45.4 bits/min on average, which is significantly higher than that of fixed stopping (FS) strategy, and the peak value of 420.2 bits/min is the highest online spelling ITR with a SSVEP-BCI up to now. The proposed system with portable EEG acquisition, friendly interaction, and alterable time of command output provides more flexibility for SSVEP-based BCIs and is promising for practical high-speed spelling.
Highlights
Brain–computer interfaces (BCIs) allow users to communicate with the external devices by converting brain signals into commands [1,2]
In order to provide flexible stimulating time, we incorporated the dynamic stopping (DS) strategy based on Bayesian posterior probability into the online state visual evoked potentials (SSVEPs)-BCI
This paper presents a series of improvement approaches for a more practical high-speed spelling system based on SSVEPs
Summary
Brain–computer interfaces (BCIs) allow users to communicate with the external devices by converting brain signals into commands [1,2]. As a kind of brain signal that owns high temporal resolution and convenience of acquisition, electroencephalogram (EEG) is welcomed by BCI researchers. Event-related potentials (ERPs) [6,7], steady-state visual evoked potentials (SSVEPs) [8,9], and event-related desynchronization/synchronization (ERD/ERS) [10,11] are typical EEG features used in BCI researches. Of these features SSVEPs that were induced by repetitive stimuli are widely employed in cognitive research and high-speed BCI systems for their high stability and signal-to-noise ratio (SNR) [12,13].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 call
