Abstract
Motion-based visual evoked potentials (mVEP) is a new emerging trend in the field of steady-state visual evoked potentials (SSVEP)-based brain–computer interfaces (BCI). In this paper, we introduce different movement-based stimulus patterns (steady-state motion visual evoked potentials—SSMVEP), without employing the typical flickering. The tested movement patterns for the visual stimuli included a pendulum-like movement, a flipping illusion, a checkerboard pulsation, checkerboard inverse arc pulsations, and reverse arc rotations, all with a spelling task consisting of 18 trials. In an online experiment with nine participants, the movement-based BCI systems were evaluated with an online four-target BCI-speller, in which each letter may be selected in three steps (three trials). For classification, the minimum energy combination and a filter bank approach were used. The following frequencies were utilized: 7.06 Hz, 7.50 Hz, 8.00 Hz, and 8.57 Hz, reaching an average accuracy between 97.22% and 100% and an average information transfer rate (ITR) between 15.42 bits/min and 33.92 bits/min. All participants successfully used the SSMVEP-based speller with all types of stimulation pattern. The most successful SSMVEP stimulus was the SSMVEP1 (pendulum-like movement), with the average results reaching 100% accuracy and 33.92 bits/min for the ITR.
Highlights
Human vision is the most important sense for us to recognize and understand the world around us; the perception of motion is the basic sensation that develops in the early stage of neural system development
In this paper we present the concept of a new design for the flicker-free steady-state motion visual evoked potentials, evaluate them with well established methods, and compare them to previously reported findings
The performance results of the online experiment, the accuracies and the information transfer rates (ITRs) Equation (8), are presented in the Table 2
Summary
Human vision is the most important sense for us to recognize and understand the world around us; the perception of motion is the basic sensation that develops in the early stage of neural system development. Within the variety of EEG-based BCI paradigms, the steady-state visual evoked potential (SSVEP) is one of the fundamental ones [1]; thanks to it’s straightforward implementation it is a common choice for speller oriented applications [2]. SSVEP-based BCI uses a constant flickering light source, e.g., LEDs or objects displayed on a computer screen usually turning on/off with a stable frequency (typically 6–90 Hz) [3]. One of the fastest online SSVEP-based BCIs was presented by Chen et al [4] in 2015. It reached an average accuracy and information transfer rate (ITR) of 99% and 267 bits/minute (bpm), with a peak ITR of 315 bpm
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