A Dynamically Optimized SSVEP Brain–Computer Interface (BCI) Speller

Abstract

The aim of this study was to design a dynamically optimized steady-state visually evoked potential (SSVEP) brain-computer interface (BCI) system with enhanced performance relative to previous SSVEP BCIs in terms of the number of items selectable on the interface, accuracy, and speed. In this approach, the row/column (RC) paradigm was employed in a SSVEP speller to increase the number of items. The target is detected by subsequently determining the row and column coordinates. To improve spelling accuracy, we added a posterior processing after the canonical correlation analysis (CCA) approach to reduce the interfrequency variation between different subjects and named the new signal processing method CCA-RV, and designed a real-time biofeedback mechanism to increase attention on the visual stimuli. To achieve reasonable online spelling speed, both fixed and dynamic approaches for setting the optimal stimulus duration were implemented and compared. Experimental results for 11 subjects suggest that the CCA-RV method and the real-time biofeedback effectively increased accuracy compared with CCA and the absence of real-time feedback, respectively. In addition, both optimization approaches for setting stimulus duration achieved reasonable online spelling performance. However, the dynamic optimization approach yielded a higher practical information transfer rate (PITR) than the fixed optimization approach. The average online PITR achieved by the proposed adaptive SSVEP speller, including the time required for breaks between selections and error correction, was 41.08 bit/min. These results indicate that our BCI speller is promising for use in SSVEP-based BCI applications.

Advancing the detection of steady-state visual evoked potentials in brain–computer interfaces

“…Since the CCA-RV method was published very recently, we could not fully and fairly compare it to the CVARS method, especially also that real-time biofeedback mechanisms were employed in [35]. However, we claim that the supervised CVARS method described in this paper should outperform the supervised CCA-RV (when we ignore the effect of the biofeedback).…”

“…Alternatively, Yin et al [35] proposed the supervised CCA-RV method for the same purpose of reducing the variability in the final target scores of the different stimulation frequencies where s cca-rv was computed with…”

“…Firstly, CVARS has been shown in the current paper to outperform CCA when results for all subjects were averaged and therefore plugging the CVARS scores instead of CCA in (21) is expected to provide, on average, better results. Additionally, LDA learns from the available training session the optimal mapping from the CVARS scores to the stimulation frequencies as it takes into account not only the mean values as it is the case in [35] but also the variances and covariations of the individual scores. Throughout this work, we had a number of channels N y = 16, which was larger than the number of signals assumed in the source model N x = 2N h = 6.…”

Design and Implementation of a Speller based on EMG Signal

“…A dynamically optimized SSVEP speller was developed by Erwei and Zongtan. They used real time biofeedback mechanism to improve accuracy [13]. Hubert Cecotti came up with a self-paced and calibration less speller.…”

Statistical evaluation of a novel SSVEP-BCI stimulation setup based on depth-of-field

“…BCI systems based on SSVEP (or SSVEP-BCI) are becoming robust systems because their response has high signal-noise rate (SNR) and are achieving high transfer rates (Allison et al, 2010;Guger et al, 2012). SSVEP-BCI systems are being employed in human-machine interaction for assistance to people with disabilities Muller et al, 2010), spellers (Yin et al, 2015) and games. However, this kind of BCI system is not suitable for patients who do not have control over their eye movements; such as patients in final stage of Amyotrophic Lateral Sclerosis (ALS).…”