Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations

Abstract

Brain-computer interface (BCI) paradigms are usually tested when environmental and biological artifacts are intentionally avoided. In this study, we deliberately introduced different perturbations in order to test the robustness of a steady state visual evoked potential (SSVEP) based BCI. Specifically we investigated to what extent a drop in performance is related to the degraded quality of EEG signals or rather due to increased cognitive load. In the online tasks, subjects focused on one of the four circles and gave feedback on the correctness of the classification under four conditions randomized across subjects: Control (no perturbation), Speaking (counting loudly and repeatedly from one to ten), Thinking (mentally counting repeatedly from one to ten), and Listening (listening to verbal counting from one to ten). Decision tree, Naïve Bayes and K-Nearest Neighbor classifiers were used to evaluate the classification performance using features generated by canonical correlation analysis. During the online condition, Speaking and Thinking decreased moderately the mean classification accuracy compared to Control condition whereas there was no significant difference between Listening and Control conditions across subjects. The performances were sensitive to the classification method and to the perturbation conditions. We have not observed significant artifacts in EEG during perturbations in the frequency range of interest except in theta band. Therefore we concluded that the drop in the performance is likely to have a cognitive origin. During the Listening condition relative alpha power in a broad area including central and temporal regions primarily over the left hemisphere correlated negatively with the performance thus most likely indicating active suppression of the distracting presentation of the playback. This is the first study that systematically evaluates the effects of natural artifacts (i.e. mental, verbal and audio perturbations) on SSVEP-based BCIs. The results can be used to improve individual classification performance taking into account effects of perturbations.

BETA: A Large Benchmark Database Toward SSVEP-BCI Application

“…Along with the rapid development of frequency recognition methods, continuous efforts have been devoted to share the SSVEP database (Bakardjian et al, 2010;Kolodziej et al, 2015;Kalunga et al, 2016;Kwak et al, 2017;Işcan and Nikulin, 2018) and contribute to public SSVEP database (Wang et al, 2017;Choi et al, 2019;Lee et al, 2019). Wang et al (2017) benchmarked a 40-target database comprising 64-channel 5-s SSVEP trials of 35 subjects who performed the offline cue-spelling task in six blocks.…”

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

“…In addition to the aforementioned target identification methods, some traditional pattern recognition methods involving classic classifiers such as LDA, SVM and k-nearest neighbour (kNN) are also usually used for SSVEP classification scheme [44], [109]. Features corresponding to different visual stimuli are regarded as the feature vector to train the classifier based on training data.…”

“…SSVEP-based BCIs are generally divided into two typical classes, named frequency-coding and phase-coding, decided by the modulation procedure and feature variable employed for classification [39]. Frequency coding system, which has the same number of stimuli and targets, uses visual stimuli with different frequencies and then examines the spectral peaks in the recorded spectrum for recognizing targets [44], [45]. Phase coding systems, designing visual stimuli with the same frequency but different phases, compare phase lags between SSVEP responses and reference ones to detect gazed target [46], [47].…”

Generalized Cross-Frequency Decomposition: A Method for the Extraction of Neuronal Components Coupled at Different Frequencies

“…To demonstrate performance of the GCFD we used EEG data obtained at the Centre for Cognition and Decision Making at Higher School of Economics (HSE, Moscow) with Steady State Visually Evoked Potentials (SSVEP), which were recorded for BCI experiments (Işcan and Nikulin, 2018 ). All the experimental procedures were approved by the local Ethics Committee.…”