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Publications

Publications by Tamas Karacsony

2020

A Deep Learning Architecture for Epileptic Seizure Classification Based on Object and Action Recognition

Authors
Karacsony, T; Loesch Biffar, AM; Vollmar, C; Noachtar, S; Cunha, JPS;

Publication
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Abstract

2019

Brain computer interface for neuro-rehabilitation with deep learning classification and virtual reality feedback

Authors
Karácsony, T; Hansen, JP; Iversen, HK; Puthusserypady, S;

Publication
ACM International Conference Proceeding Series

Abstract
Though Motor Imagery (MI) stroke rehabilitation effectively promotes neural reorganization, current therapeutic methods are immeasurable and their repetitiveness can be demotivating. In this work, a real-time electroencephalogram (EEG) based MI-BCI (Brain Computer Interface) system with a virtual reality (VR) game as a motivational feedback has been developed for stroke rehabilitation. If the subject successfully hits one of the targets, it explodes and thus providing feedback on a successfully imagined and virtually executed movement of hands or feet. Novel classification algorithms with deep learning (DL) and convolutional neural network (CNN) architecture with a unique trial onset detection technique was used. Our classifiers performed better than the previous architectures on datasets from PhysioNet offline database. It provided fine classification in the real-time game setting using a 0.5 second 16 channel input for the CNN architectures. Ten participants reported the training to be interesting, fun and immersive. "It is a bit weird, because it feels like it would be my hands", was one of the comments from a test person. The VR system induced a slight discomfort and a moderate effort for MI activations was reported. We conclude that MI-BCI-VR systems with classifiers based on DL for real-time game applications should be considered for motivating MI stroke rehabilitation. © 2019 Association for Computing Machinery.

2021

DeepEpil: Towards an Epileptologist-Friendly AI Enabled Seizure Classification Cloud System based on Deep Learning Analysis of 3D videos

Authors
Karacsony, T; Loesch Biffar, AM; Vollmar, C; Noachtar, S; Cunha, JPS;

Publication
2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

Abstract