Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms

TL;DR

This paper introduces a comprehensive multimodal dataset combining EEG, eye-tracking, and high-speed video recordings across multiple BCI paradigms, enabling advanced analysis of ocular activity and artifact handling.

Contribution

The dataset uniquely integrates EEG, eye-tracking, and high-speed video data across four BCI paradigms, facilitating research on ocular activity and artifact management.

Findings

Over 46 hours of data from 31 subjects collected.

Includes data for motor imagery, motor execution, SSVEP, and P300 paradigms.

Supports development of algorithms for artifact removal and cross-paradigm analysis.

Abstract

In Brain-Computer Interface (BCI) research, the detailed study of blinks is crucial. They can be considered as noise, affecting the efficiency and accuracy of decoding users' cognitive states and intentions, or as potential features, providing valuable insights into users' behavior and interaction patterns. We introduce a large dataset capturing electroencephalogram (EEG) signals, eye-tracking, high-speed camera recordings, as well as subjects' mental states and characteristics, to provide a multifactor analysis of eye-related movements. Four paradigms -- motor imagery, motor execution, steady-state visually evoked potentials, and P300 spellers -- are selected due to their capacity to evoke various sensory-motor responses and potential influence on ocular activity. This online-available dataset contains over 46 hours of data from 31 subjects across 63 sessions, totaling 2520 trials for each of the first three paradigms, and 5670 for P300. This multimodal and multi-paradigms dataset is expected to allow the development of algorithms capable of efficiently handling eye-induced artifacts and enhancing task-specific classification. Furthermore, it offers the opportunity to evaluate the cross-paradigm robustness involving the same participants.