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Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions From Limited Data



A Deep Classifier for Upper-Limbs Motor Anticipation Tasks in an Online BCI Setting


Decoding Motor sentence examples within decoding motor imagery



Exploring a Deeper Convolutional Neural Network Architecture with high dropout for Motor Imagery BCI Decoding



Real-time Human-like Robot Control based on Brain-Computer Interface


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10.1109/BCI51272.2021.9385323

Exploring a Deeper Convolutional Neural Network Architecture with high dropout for Motor Imagery BCI Decoding


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10.1109/RCAR52367.2021.9517441

Detecting Movement Timing from Primate Intracortical Signals for Brain-machine Interfaces in Dynamic Environment


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10.1109/NER49283.2021.9441172

Phase component of frequency-domain functional near-infrared imaging improves decoding of motor-evoked neural activity


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10.1109/TNSRE.2021.3083755

Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions From Limited Data


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10.1038/s41597-021-00883-1

Continuous sensorimotor rhythm based brain computer interface learning in a large population


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10.3390/bioengineering8020021

A Deep Classifier for Upper-Limbs Motor Anticipation Tasks in an Online BCI Setting


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10.1109/IHSH51661.2021.9378751

Real-time Human-like Robot Control based on Brain-Computer Interface


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10.1109/TBME.2021.3063903

Force Decoding of Caudal Forelimb Area and Rostral Forelimb Area in Chronic Stroke Rats


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10.1088/1741-2552/abef3a

Computational approaches to decode grasping force and velocity level in upper-limb amputee from intraneural peripheral signals


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10.1088/1741-2552/aaf046

A penalized time-frequency band feature selection and classification procedure for improved motor intention decoding in multichannel EEG.



Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals


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10.1088/1741-2552/aaed85

Decoding motor neuron activity from epimysial thin-film electrode recordings following targeted muscle reinnervation.


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10.1109/NER.2019.8716947

Influence of Frequency Bands in EEG Signal to Predict User Intent


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