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.


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    Titel :

    Brain Computer Interface for Neuro-rehabilitation With Deep Learning Classification and Virtual Reality Feedback



    Erscheinungsdatum :

    2019-01-01


    Anmerkungen:

    Karácsony , T , Hansen , J P , Iversen , H K & Puthusserypady , S 2019 , Brain Computer Interface for Neuro-rehabilitation With Deep Learning Classification and Virtual Reality Feedback . in Proceedings of the 10th Augmented Human International Conference 2019 . , Article No. 22 , Association for Computing Machinery , 10th Augmented Human International Conference 2019 , Reims , France , 11/03/2019 . https://doi.org/10.1145/3311823.3311864



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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