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.
Effects of mental workload and fatigue on the p300, alpha and theta band power during operation of an erp (p300) brain–computer interface,
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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.