BNCI 2022-001 EEG Correlates of Difficulty Level dataset
This dataset comprises EEG recordings from 13 healthy subjects performing a visuomotor learning task involving simulated drone piloting through waypoints of varying difficulty levels. The study investigates real-time decoding of subjective difficulty from EEG signals to enable adaptive closed-loop learning, comparing algorithmic difficulty adjustment with subject-controlled progression. Data include 1 offline session and 2 online sessions (online_session_2, online_session_3) with preprocessed EEG recordings (64 channels + 3 EOG, 25 central EEG channels retained after preprocessing) sampled at 256 Hz, along with behavioral markers of task performance including waypoint hits/misses and trajectory events.
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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.