The paper reports that an attention-based LSTM, trained on 297 hand-crafted EEG features per time step, reaches 83.2% cross-subject and 98.3% intra-subject accuracy for left/right hand movement classification on the PhysioNet EEG Movement dataset.
Brain–computer interfaces in neurological rehabilitation,
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Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network
The paper reports that an attention-based LSTM, trained on 297 hand-crafted EEG features per time step, reaches 83.2% cross-subject and 98.3% intra-subject accuracy for left/right hand movement classification on the PhysioNet EEG Movement dataset.