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Found 22 papers, 2 papers with code
READS-V: Real-time Automated Detection of Epileptic Seizures from Surveillance Videos via Skeleton-based Spatiotemporal ViG
An accurate and efficient epileptic seizure onset detection system can significantly benefit patients.
CMISR: Circular Medical Image Super-Resolution
Five CMISR algorithms are respectively proposed based on the state-of-the-art open-loop MISR algorithms.
A 137.5 TOPS/W SRAM Compute-in-Memory Macro with 9-b Memory Cell-Embedded ADCs and Signal Margin Enhancement Techniques for AI Edge Applications
In this paper, we propose a high-precision SRAM-based CIM macro that can perform 4x4-bit MAC operations and yield 9-bit signed output.
CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution
Closed-loop negative feedback mechanism is extensively utilized in automatic control systems and brings about extraordinary dynamic and static performance.
Shorter Latency of Real-time Epileptic Seizure Detection via Probabilistic Prediction
And, a novel multiscale STFT-based feature extraction method combined with 3D-CNN architecture is proposed to accurately capture predictive probabilities of samples.
A 97 fJ/Conversion Neuron-ADC with Reconfigurable Sampling and Static Power Reduction
A bio-inspired Neuron-ADC with reconfigurable sampling and static power reduction for biomedical applications is proposed in this work.
Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond
In this survey, we assess the eligibility of more than fifty published peer-reviewed representative transfer learning approaches for EMG applications.
A Compact Online-Learning Spiking Neuromorphic Biosignal Processor
In this design, a compact online learning neuromorphic hardware architecture with ultra-low power consumption designed explicitly for biosignal processing is proposed.
SpikeSEE: An Energy-Efficient Dynamic Scenes Processing Framework for Retinal Prostheses
In this paper, we propose an energy-efficient dynamic scenes processing framework (SpikeSEE) that combines a spike representation encoding technique and a bio-inspired spiking recurrent neural network (SRNN) model to achieve intelligent processing and extreme low-power computation for retinal prostheses.
ICRICS: Iterative Compensation Recovery for Image Compressive Sensing
The proposed method depends on any existing approaches and upgrades their reconstruction performance by adding negative feedback structure.
Binary Single-dimensional Convolutional Neural Network for Seizure Prediction
Nowadays, several deep learning methods are proposed to tackle the challenge of epileptic seizure prediction.
Recent Trends and Future Prospects of Neural Recording Circuits and Systems: A Tutorial Brief
Recent years have seen fast advances in neural recording circuits and systems as they offer a promising way to investigate real-time brain monitoring and the closed-loop modulation of psychological disorders and neurodegenerative diseases.
An Event-Driven Compressive Neuromorphic System for Cardiac Arrhythmia Detection
Wearable electrocardiograph (ECG) recording and processing systems have been developed to detect cardiac arrhythmia to help prevent heart attacks.
Multichannel Synthetic Preictal EEG Signals to Enhance the Prediction of Epileptic Seizures
To identify the preictal region that precedes the onset of seizure, a large number of annotated EEG signals are required to train DL algorithms.
neuro2vec: Masked Fourier Spectrum Prediction for Neurophysiological Representation Learning
Extensive data labeling on neurophysiological signals is often prohibitively expensive or impractical, as it may require particular infrastructure or domain expertise.
Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation
The proposed training scheme significantly improves the performance of patient-specific seizure predictors and bridges the gap between patient-specific and patient-independent predictors.
C$^2$SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction
Although compressive sensing (CS) can be adopted to compress the signals to reduce communication bandwidth requirement, it needs a complex reconstruction procedure before the signal can be used for seizure prediction.
An End-to-End Deep Learning Approach for Epileptic Seizure Prediction
Conventional seizure prediction works usually rely on features extracted from Electroencephalography (EEG) recordings and classification algorithms such as regression or support vector machine (SVM) to locate the short time before seizure onset.
Cascade Decoders-Based Autoencoders for Image Reconstruction
It is evaluated by the experimental results that the proposed autoencoders outperform the classical autoencoders in the performance of image reconstruction.
A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction
Accurate prediction of epileptic seizures allows patients to take preventive measures in advance to avoid possible injuries.
A New Neuromorphic Computing Approach for Epileptic Seizure Prediction
Motivated by the energy-efficient spiking neural networks (SNNs), a neuromorphic computing approach for seizure prediction is proposed in this work.
Spatial Resolution of Local Field Potential Signals in Macaque V4
The key characteristic of such a system is the ability to discriminate between responses to different positions in the visual field.
