4.4 Article

Epilepsy Detection with Multi-channel EEG Signals Utilizing AlexNet

Related references

Note: Only part of the references are listed.
Article Physics, Multidisciplinary

Robust Spike-Based Continual Meta-Learning Improved by Restricted Minimum Error Entropy Criterion

Shuangming Yang et al.

Summary: This research proposes a novel spike-based framework called MeMEE, which uses entropy theory to establish a gradient-based online meta-learning scheme in a recurrent SNN architecture. The proposed MeMEE model effectively improves the accuracy and robustness of spike-based meta-learning performance. This research provides new perspectives for integrating information theory into machine learning to enhance the learning performance of SNNs.

ENTROPY (2022)

Article Neurosciences

Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning

Shuangming Yang et al.

Summary: This paper proposes a novel spike-based framework called HESFOL, which uses the entropy theory to establish a gradient-based few-shot learning scheme in a recurrent SNN architecture. Experimental results show that HESFOL can effectively improve the accuracy and robustness of spike-driven few-shot learning performance.

FRONTIERS IN NEUROSCIENCE (2022)

Article Neurosciences

SAM: A Unified Self-Adaptive Multicompartmental Spiking Neuron Model for Learning With Working Memory

Shuangming Yang et al.

Summary: This article presents a novel neuron model called SAM, which integrates spike-based learning with working memory. The experimental results demonstrate the energy efficiency and robustness of SAM in various challenging tasks. The study also explores the effects of SAM model variations on its working memory, aiming to uncover the biological mechanisms of working memory in the brain.

FRONTIERS IN NEUROSCIENCE (2022)

Article Computer Science, Artificial Intelligence

Neuromorphic Context-Dependent Learning Framework With Fault-Tolerant Spike Routing

Shuangming Yang et al.

Summary: This study introduces a scalable hardware framework for fault-tolerant context-dependent learning in neuromorphic computing, demonstrating an improvement in network throughput. The proposed system can be utilized for real-time decision-making, brain-machine integration, and research on brain cognition during learning.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

BiCoSS: Toward Large-Scale Cognition Brain With Multigranular Neuromorphic Architecture

Shuangming Yang et al.

Summary: The article introduces a biologically-inspired cognitive supercomputing system, BiCoSS, which integrates multiple GRs of SNNs to create a hybrid neuromorphic platform with efficient and scalable architecture and low power consumption. The system has successfully replicated various biological cognitive activities, demonstrating its high performance and potential applications.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

Article Neurosciences

Neural fragility as an EEG marker of the seizure onset zone

Adam Li et al.

Summary: Neural fragility, a networked dynamic system biomarker, is developed as a more robust tool for localizing seizures in epilepsy patients compared to traditional features. The study shows that neural fragility outperforms clinicians in predicting surgical outcomes, suggesting its potential as an EEG biomarker for the seizure onset zone.

NATURE NEUROSCIENCE (2021)

Article Computer Science, Artificial Intelligence

Scalable Digital Neuromorphic Architecture for Large-Scale Biophysically Meaningful Neural Network With Multi-Compartment Neurons

Shuangming Yang et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)

Article Mathematics, Interdisciplinary Applications

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

Fabio Pisano et al.

COMPLEXITY (2020)

Proceedings Paper Computer Science, Theory & Methods

Novel Casestudy and Benchmarking of AlexNet for Edge AI: From CPU and GPU to FPGA

Firas Al-Ali et al.

2020 IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE) (2020)

Article Computer Science, Artificial Intelligence

Epileptic seizure detection using hybrid machine learning methods

Abdulhamit Subasi et al.

NEURAL COMPUTING & APPLICATIONS (2019)

Review Computer Science, Theory & Methods

Characterization of focal EEG signals: A review

U. Rajendra Acharya et al.

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE (2019)

Article Multidisciplinary Sciences

iEEG-BIDS, extending the Brain Imaging Data Structure specification to human intracranial electrophysiology

Christopher Holdgraf et al.

SCIENTIFIC DATA (2019)

Article Multidisciplinary Sciences

EEG-BIDS, an extension to the brain imaging data structure for electroencephalography

Cyril R. Pernet et al.

SCIENTIFIC DATA (2019)

Article Computer Science, Artificial Intelligence

Auto-detection of epileptic seizure events using deep neural network with different feature scaling techniques

D. K. Thara et al.

PATTERN RECOGNITION LETTERS (2019)

Article Engineering, Biomedical

Focal Onset Seizure Prediction Using Convolutional Networks

Haidar Khan et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2018)

Article Computer Science, Artificial Intelligence

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram

Nhan Duy Truong et al.

NEURAL NETWORKS (2018)

Article Biology

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals

U. Rajendra Acharya et al.

COMPUTERS IN BIOLOGY AND MEDICINE (2018)

Review Clinical Neurology

Seizure prediction - ready for a new era

Levin Kuhlmann et al.

NATURE REVIEWS NEUROLOGY (2018)

Review Behavioral Sciences

Automated seizure prediction

U. Rajendra Acharya et al.

EPILEPSY & BEHAVIOR (2018)

Article Mathematical & Computational Biology

Epileptic Seizure Detection Based on EEG Signals and CNN

Mengni Zhou et al.

FRONTIERS IN NEUROINFORMATICS (2018)

Article Engineering, Biomedical

Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features

Thea Raduentz et al.

JOURNAL OF NEURAL ENGINEERING (2017)

Article Computer Science, Artificial Intelligence

A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension

Manish Sharma et al.

PATTERN RECOGNITION LETTERS (2017)

Article Engineering, Biomedical

Adaptive Epileptic Seizure Prediction Based on EEG Synchronization

Sutrisno Ibrahim et al.

JOURNAL OF BIOMIMETICS BIOMATERIALS AND BIOMEDICAL ENGINEERING (2017)

Article Computer Science, Hardware & Architecture

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky et al.

COMMUNICATIONS OF THE ACM (2017)

Review Engineering, Biomedical

Towards accurate prediction of epileptic seizures: A review

Elie Bou Assi et al.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2017)

Review Biochemical Research Methods

Seizure prediction for therapeutic devices: A review

Kais Gadhoumi et al.

JOURNAL OF NEUROSCIENCE METHODS (2016)

Article Engineering, Electrical & Electronic

Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network

Yatindra Kumar et al.

SIGNAL IMAGE AND VIDEO PROCESSING (2014)

Article Biochemical Research Methods

Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine

Yuedong Song et al.

JOURNAL OF NEUROSCIENCE METHODS (2012)

Article Clinical Neurology

Emergent network topology at seizure onset in humans

Mark A. Kramer et al.

EPILEPSY RESEARCH (2008)