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Found 7 papers, 1 papers with code
Feed-forward and recurrent inhibition for compressing and classifying high dynamic range biosignals in spiking neural network architectures
Neuromorphic processors that implement Spiking Neural Networks (SNNs) using mixed-signal analog/digital circuits represent a promising technology for closed-loop real-time processing of biosignals.
Long-term stable Electromyography classification using Canonical Correlation Analysis
Here we propose a novel statistical method based on canonical correlation analysis (CCA) that stabilizes EMG classification performance across multiple days for long-term control of prosthetic devices.
Neuromorphic implementation of ECG anomaly detection using delay chains
Real-time analysis and classification of bio-signals measured using wearable devices is computationally costly and requires dedicated low-power hardware.
Organic log-domain integrator synapse
In particular, the circuit is fabricated using organic-based materials that are electrically active, offer flexibility and biocompatibility, as well as time constants (critical in learning neural codes and encoding spatiotemporal patterns) that are biologically plausible.
Closed-loop spiking control on a neuromorphic processor implemented on the iCub
In this paper, we present a closed-loop motor controller implemented on mixed-signal analog-digital neuromorphic hardware using a spiking neural network.
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge.
Sensor fusion using EMG and vision for hand gesture classification in mobile applications
The discrimination of human gestures using wearable solutions is extremely important as a supporting technique for assisted living, healthcare of the elderly and neurorehabilitation.
