BNCI 2014-001 Motor Imagery dataset

Résumé fourni par la source

The BNCI 2014-001 Motor Imagery dataset is a widely-used benchmark for brain-computer interface research, comprising EEG recordings from 9 healthy subjects performing four-class motor imagery tasks (left hand, right hand, feet, and tongue). Each subject completed two sessions with 6 runs per session, yielding 200 training and 240 test trials. The dataset features 22 EEG channels and 3 EOG channels sampled at 250 Hz. Data are provided in two versions: original at 250 Hz and downsampled to 100 Hz using Chebyshev Type II filtering (order 10, stop band ripple 50 dB, stop band edge 49 Hz). Preprocessing includes bandpass filtering (0.05-200 Hz) and 50 Hz notch filtering, making it a standard resource for evaluating multi-class motor imagery classification algorithms and cross-session transfer learning approaches.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.