A User-Friendly Dictionary-Supported SSVEP-based BCI Application

Abstract

Abstract. A brain-computer interface (BCI) measures and interprets brain signals enabling people to communicate without the use of peripheral muscles. One of the common BCI paradigms are steady state visual evoked potentials (SSVEPs), brain signals induced by gazing at a constantly flickering target. The choice of stimulation frequencies and the number of simultaneously used stimuli highly influence the performance of such SSVEP-based BCI. In this article, a dictionary-driven four class SSVEP-based spelling application is presented, tested, and evaluated. To enhance classification accuracy, frequencies were determined individually with a calibration software for SSVEP-BCIs, enabling non-experts to set up the system. Forty-one healthy participants used the BCI system to spell English sentences (lengths between 23 and 37 characters). All participants completed the spelling task successfully. A mean accuracy of 97.92% and a mean ITR of 23.84 bits/min were achieved, 18 participants even reached 100% accuracy. On average the number of commands needed to spell the example sentences with four classes, without dictionary support is higher by a factor of 1.92. Thanks to the implemented dictionary the time needed to spell typical everyday sentences can be drastically reduced.

Phase-Spatial Beamforming Renders a Visual Brain Computer Interface Capable of Exploiting EEG Electrode Phase Shifts in Motion-Onset Target Responses

“…As these experiments were performed on healthy participants, a potentially higher communciation speed can be achieved when decoding user intention from eye-tracking data (e.g., compared to SSVEP [50] and compared to P300 [51]). But this might not be the case with certain patient groups.…”

State of the Art of Non-Invasive Electrode Materials for Brain–Computer Interface

“…Mental illnesses such as depression can also be treated with BCI [11]. In addition, BCI can also test the difficulty of working in different environments, such as predicting the difficulty of playing Tetris by EEG activity [12], evaluating the pressure load changes of surgeons during simple and complex surgical training tasks [13], detecting drivers' fatigue degree during driving [14], and evaluating the sustained attention of real word classroom students [15]. BCI also has some preliminary applications in the field of games [16], such as using EEG to control archery [17] and the realization of multiplayer online VR racing games through BCI [18].…”