EEG dataset of consumer- and research-grade systems

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Consumer-grade electroencephalography (EEG) devices are increasingly prevalent, necessitating rigorous evaluation against established research-grade equipment.
• Standardized datasets are crucial for objectively assessing the performance and reliability of new EEG technologies.

Purpose of the Study:

• To introduce a comprehensive, open-access electroencephalography (EEG) dataset for comparing the performance of multiple consumer-grade EEG devices against a research-grade device.
• To facilitate the validation of signal quality, neural feature extraction (e.g., alpha-band activity), and artifact robustness of consumer EEG devices.

Main Methods:

• Collected EEG data from 30 participants using four consumer devices (BrainLink Pro, NeuroNicle FX2, MindWave Mobile 2, Muse 2) and one research-grade device (DSI-24).
• Acquired data across four experimental paradigms: eye blinks, jaw clenching, head movements (eyes open/closed), with resting-state EEG recorded pre- and post-task.
• Utilized a multi-channel research-grade device (DSI-24) as a benchmark for comparison.

Main Results:

• The dataset provides a benchmark for evaluating consumer EEG device signal integrity and performance.
• Enables detailed analysis of neural features, such as alpha-band power, across different devices.
• Facilitates the assessment of device susceptibility to movement-induced artifacts, a common challenge in real-world EEG applications.

Conclusions:

• The presented open EEG dataset is a valuable resource for researchers and developers in the field of neurotechnology.
• It supports the comparative evaluation of consumer-grade EEG devices, promoting advancements in accessible brain-computer interfaces.
• The dataset's availability on Figshare encourages further research into EEG device validation and application development.