WearBCI Dataset: Understanding and Benchmarking Real-World Wearable Brain-Computer Interfaces Signals

TL;DR

The WearBCI dataset provides comprehensive, multimodal recordings of wearable BCI signals under various motion conditions, enabling evaluation and benchmarking of signal quality and artifact mitigation in real-world scenarios.

Contribution

This work introduces the first dataset with synchronized EEG, IMU, and egocentric video under diverse motion dynamics, along with systematic benchmarks and case studies for wearable BCI research.

Findings

Motion artifacts significantly impact wearable EEG signals across different activities.

Benchmarking reveals the effectiveness of various EEG signal enhancement techniques.

Cross-modal approaches improve EEG signal quality and human behavior understanding.

Abstract

Brain-computer interfaces (BCIs) have opened new platforms for human-computer interaction, medical diagnostics, and neurorehabilitation. Wearable BCI systems, which typically employ non-invasive electrodes for portable monitoring, hold great promise for real-world applications, but also face significant challenges of signal quality degradation caused by motion artifacts and environmental interferences. Most existing wearable BCI datasets are collected under stationary or controlled lab settings, limiting their utility for evaluating performance under body movement. To bridge this gap, we introduce WearBCI, the first dataset that comprehensively evaluates wearable BCI signals under different motion dynamics with synchronized multimodal recordings (EEG, IMU, and egocentric video), and systematic benchmark evaluations for studying impacts of motion artifact. Specifically, we collect data from 36 participants across different motion dynamics, including body movements, walking, and navigation. This dataset includes synchronized electroencephalography (EEG), inertial measurement unit (IMU) data, and egocentric video recordings. We analyze the collected wearable EEG signals to understand the impact of motion artifacts across different conditions, and benchmark representative EEG signal enhancement techniques on our dataset. Furthermore, we explore two new case studies: cross-modal EEG signal enhancement and multi-dimension human behavior understanding. These findings offer valuable insights into real-world wearable BCI deployment and new applications.