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Publications by Tamas Karacsony


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

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

ACM International Conference Proceeding Series

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.


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

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

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