nm000161 NEMAR-native dataset
BNCI 2024-001 Handwritten Character Classification dataset
This dataset comprises EEG recordings from 20 healthy participants performing motor imagery of handwritten letter production. Participants imagined writing ten different letters (a, d, e, f, j, n, o, s, t, v) using their right index finger in response to visual cues. The study investigates brain-computer interface applications through direct EEG classification and continuous kinematic decoding approaches, achieving classification accuracies of 26.2% for ten-letter and 46.7% for five-letter tasks.
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Coming soon. Per-file data-quality summaries are precomputed by the NEMAR processing pipeline. The static aggregate is on the way — tracked at nemar-cli#511.