John Thomas

Orcid: 0000-0003-0144-3746

Affiliations:
  • Rochester Institute of Technology, Department of Electrical and Computer Engineering Technology, Rochester, NY, USA
  • McGill University, Montreal Neurological Institut, Montreal, Canada
  • Nanyang Technological University (NTU), School of Electrical and Electronic Engineering, Singapore


According to our database1, John Thomas authored at least 17 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Automated Multi-Class Seizure-Type Classification System Using EEG Signals and Machine Learning Algorithms.
IEEE Access, 2024

2023
Six-Center Assessment of CNN-Transformer with Belief Matching Loss for Patient-Independent Seizure Detection in EEG.
Int. J. Neural Syst., March, 2023

Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings.
Sensors, January, 2023

Low Valence Low Arousal Stimuli: An Effective Candidate for EEG-Based Biometrics Authentication System.
Proceedings of the Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023, Gothenburg, Sweden, 22, 2023

Optimization of Pre-Ictal Interval Time Period for Epileptic Seizure Prediction Using Temporal and Frequency Features.
Proceedings of the Caring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023, Gothenburg, Sweden, 22, 2023

Multi-Class Seizure Type Classification Using Features Extracted from the EEG.
Proceedings of the Healthcare Transformation with Informatics and Artificial Intelligence, 2023

2021
Automated Adult Epilepsy Diagnostic Tool Based on Interictal Scalp Electroencephalogram Characteristics: A Six-Center Study.
Int. J. Neural Syst., 2021

Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.
Int. J. Neural Syst., 2021

Multi-Center Validation Study of Automated Classification of Pathological Slowing in Adult Scalp Electroencephalograms Via Frequency Features.
Int. J. Neural Syst., 2021

2020
Automated Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms by Convolutional Neural Networks.
Int. J. Neural Syst., 2020

Deep Learning for Interictal Epileptiform Spike Detection from scalp EEG frequency sub bands.
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020

2018
EEG CLassification Via Convolutional Neural Network-Based Interictal Epileptiform Event Detection.
Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018

A deep Learning Scheme for Automatic Seizure Detection from Long-Term Scalp EEG.
Proceedings of the 52nd Asilomar Conference on Signals, Systems, and Computers, 2018

2017
Neurology-as-a-Service for the Developing World.
CoRR, 2017

Deep learning-based classification for brain-computer interfaces.
Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics, 2017

Automated epileptiform spike detection via affinity propagation-based template matching.
Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017

2016
Clustering of interictal spikes by dynamic time warping and affinity propagation.
Proceedings of the 2016 IEEE International Conference on Acoustics, 2016


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