SSVEP detection assessment by combining visual stimuli paradigms and no-training detection methods

Abstract

IntroductionBrain-Computer Interfaces (BCI) based on Steady-State Visually Evoked Potentials (SSVEP) have great potential for use in communication applications because of their relatively simple assembly and in some cases the possibility of bypassing the time-consuming training stage. However, among multiple factors, the efficient performance of this technology is highly dependent on the stimulation paradigm applied in combination with the SSVEP detection algorithm employed. This paper proposes the performance assessment of the classification of target events with respect to non-target events by applying four types of visual paradigms, rectangular modulated On-Off (OOR), sinusoidal modulated On-Off (OOS), rectangular modulated Checkerboard (CBR), and sinusoidal modulated Checkerboard (CBS), with three types of SSVEP detection methods, Canonical Correlation Analysis (CCA), Filter-Bank CCA (FBCCA), and Minimum Energy Combination (MEC).MethodsWe set up an experimental protocol in which the four types of visual stimuli were presented randomly to twenty-seven participants and after acquiring their electroencephalographic responses to five stimulation frequencies (8.57, 10.909, 15, 20, and 24 Hz), the three detection methods were applied to the collected data.ResultsThe results are conclusive, obtaining the best performance with the combination of either OOR or OOS visual stimulus and the FBCCA as a detection method, however, this finding contrasts with the opinion of almost half of the participants in terms of visual comfort, where the 51.9% of the subjects felt more comfortable and focused with CBR or CBS stimulation.DiscussionFinally, the EEG recordings correspond to the SSVEP response of 27 subjects to four visual paradigms when selecting five items on a screen, which is useful in BCI navigation applications. The dataset is available to anyone interested in studying and evaluating signal processing and machine-learning algorithms for SSVEP-BCI systems.

TSPNet: a time-spatial parallel network for classification of EEG-based multiclass upper limb motor imagery BCI

“…Additionally, Zhang et al (2020) proposed a deep learning framework that incorporates convolutional and recurrent neural networks. EEG-based BCI applications commonly rely on four main types of neurophysiological patterns, namely, steady-state visual evoked potential (SSVEP) (Autthasan et al, 2020;Kwak and Lee, 2020;Rivera-Flor et al, 2022;Zhang et al, 2022;Chailloux Peguero et al, 2023;Yan et al, 2023), event-related potential (ERP) (Cecotti and Graeser, 2011;Zou et al, 2016;Li et al, 2020), movement-related cortical potentials (MRCPs) (Xu et al, 2014;Jeong et al, 2020), and motor imagery (MI) (Siuly and Li, 2012;Higashi and Tanaka, 2013;Edelman et al, 2016;He et al, 2016;Chaisaen et al, 2020;Wu et al, 2020;Gaur et al, 2021;Ma et al, 2022;Fan et al, 2023;Zhang et al, 2023). Among these EEG applications, MI has garnered increasing attention within BCI systems due to its ability to elicit oscillatory neural activity in specific frequency bands over the motor cortex region without external stimuli.…”