nASR is an end-to-end trainable Keras layer for channel-level EEG artifact subspace reconstruction that outperforms traditional ASR with 6-8x faster inference on BCI Competition IV data.
Managing electromyogram contamination in scalp recordings: An approach identifying reliable beta and gamma eeg features of psychoses or other disorders,
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
RDWT-driven deep neural network processes EEG during AR-VOMS tasks to predict ocular response times via DTW, showing task-dependent behaviors and inter-subject differences for mTBI assessment.
citing papers explorer
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nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
nASR is an end-to-end trainable Keras layer for channel-level EEG artifact subspace reconstruction that outperforms traditional ASR with 6-8x faster inference on BCI Competition IV data.
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BCI-Based Assessment of Ocular Response Time Using Dynamic Time Warping Leveraging an RDWT-Driven Deep Neural Framework
RDWT-driven deep neural network processes EEG during AR-VOMS tasks to predict ocular response times via DTW, showing task-dependent behaviors and inter-subject differences for mTBI assessment.