Mainsah et al. 2025 — bigP3BCI: An Open, Diverse and Machine Learning Ready P300-based Brain-Computer Interface Dataset (Study G)
[, NIRx NIRScout, 7.8 Hz, SNIRF format. 3 conditions x 30 trials. Zenodo DOI: 10.5281/zenodo.6575155
[, galvanic skin response (GSR), and electrocardiogram (ECG) recordings from 17 healthy participants during an affective music brain-computer interface training study. Participants listened to 40-second music clips (20s per emotional state) designed to induce specific emotional states across three sessions, with self-reported valence and arousal ratings. The data supports the development and validation of music-based brain-computer interfaces for monitoring and inducing affective states. This is the training session dataset; two additional datasets cover system calibration and online real-time control phases.
[ dataset investigates the differential neural mechanisms underlying selection and maintenance of information during working memory tasks. Data from 22 participants include MEG recordings across two sessions, structural MRI, and detailed behavioral measures from a working memory task and a one-back control task involving visual Gabor stimuli. The dataset supports investigation of how the brain selectively maintains task-relevant information while filtering distractions.