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About

I am a researcher in the area of machine learning at the BRAIN group. I received my first master degree in Biomedical Engineering from the Technical University of Denmark. My other masters degree in Mechatronics is from the Budapest University of Technology and Economics.

My main research interest include:

  • Neuroengineering
  • Action and pattern recognition
  • Exciting applications of ML, mainly in the healthcare sector
  • Computer vision
  • Signal processing
  • Real-time biomedical systems
  • Physiological modelling.

Interest
Topics
Details

Details

Publications

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.