Abstract
Visual evoked potentials (VEPs) are used in clinical applications in ophthalmology, neurology, and extensively in brain–computer interface (BCI) research. Many BCI implementations utilize steady-state VEP (SSVEP) and/or code modulated VEP (c-VEP) as inputs, in tandem with sophisticated methods to improve information transfer rates (ITR). There is a gap in knowledge regarding the adaptation dynamics and physiological generation mechanisms of the VEP response, and the relation of these factors with BCI performance. A simple, dual pattern display setup was used to evoke VEPs and to test signatures elicited by non-isochronic, non-singular, low jitter stimuli at the rates of 10, 32, 50, and 70 reversals per second (rps). Non-isochronic, low-jitter stimulation elicits quasi-steady-state VEPs (QSS-VEPs) that are utilized for the simultaneous generation of transient VEP and QSS-VEP. QSS-VEP is a special case of c-VEPs, and it is assumed that it shares similar generators of the SSVEPs. Eight subjects were recorded, and the performance of the overall system was analyzed using receiver operating characteristic (ROC) curves, accuracy plots, and ITRs. In summary, QSS-VEPs performed better than transient VEPs (TR-VEP). It was found that in general, 32 rps stimulation had the highest ROC area, accuracy, and ITRs. Moreover, QSS-VEPs were found to lead to higher accuracy by template matching compared to SSVEPs at 32 rps. To investigate the reasons behind this, adaptation dynamics of transient VEPs and QSS-VEPs at all four rates were analyzed and speculated.
Highlights
Brain–computer interface (BCI) research has shown remarkable progress as witnessed by an increasing number of publications
In this study, we developed a dual-display brain–computer interface (BCI) gaze sensor based on specially designed low-jitter Pseudo random binary sequences (PRBS) codes eliciting QSS-Visual evoked potentials (VEPs) and adopted the template matching as the detection method
It was found that statistically there was no significant difference in the mean accuracies by left or right gaze using single sweep (0.5 s) quasi-steady-state-visual evoked potentials (QSS-VEP) signature at all rates
Summary
Brain–computer interface (BCI) research has shown remarkable progress as witnessed by an increasing number of publications. BCI provides a direct communication and control channel between the human brain and output devices to achieve a desired output function by using the control signals derived from the human brain [5]. Depending on the signal acquisition methods, Electroencephalography (EEG) based BCIs are gaining increased attention due to non-invasiveness and practical EEG headsets available on the market [6]. In EEG based BCI applications, there are four main signal classes, namely slow cortical potentials (SCP), sensorimotor rhythms (SMR), P300 evoked potentials and visual evoked potentials (VEP) [1,2,3,5,7]. VEP based BCIs paradigms are common due to several advantages. These are mainly high ITR, simple setup, and little if any user training [8]
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