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Real-Time EMG Signal Classification via Recurrent Neural Networks
Real-time classification of Electromyography signals is the most challenging part of controlling a prosthetic hand.
EMG-Based Feature Extraction and Classification for Prosthetic Hand Control
The experimental results illustrate that the proposed method enhances the accuracy of real-time classification of EMG signals up to $95. 5\%$ for $800$ msec signal length.
EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine
Electromyography is a promising approach to the gesture recognition of humans if an efficient classifier with high accuracy is available.
Analog Seizure Detection for Implanted Responsive Neurostimulation
Similarly, power calculations were performed, demonstrating that the system uses $6. 5 \mu W$ per channel, which when compared to the state-of-the-art NeuroPace system would increase battery life by up to $50 \%$.
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