Name
Title | ||
|---|---|---|
Brain Computer Interface for Neuro-rehabilitation With Deep Learning Classification and Virtual Reality Feedback |
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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3311823.3311864 | Proceedings of the 10th Augmented Human International Conference 2019 |
Keywords | Field | DocType |
Brain Computer Interface, CNN, Deep learning, Motor Imagery, Online EEG classification, Virtual Reality | Rehabilitation,Computer vision,Virtual reality,Computer science,Convolutional neural network,Brain–computer interface,Human–computer interaction,Immersion (virtual reality),Artificial intelligence,Deep learning,Statistical classification,Motor imagery | Conference |
ISBN | Citations | PageRank |
978-1-4503-6547-5 | 1 | 0.48 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tamás Karácsony | 1 | 1 | 0.48 |
| John Paulin Hansen | 2 | 560 | 65.39 |
| Helle K Iversen | 3 | 18 | 3.56 |
| Sadasivan Puthusserypady | 4 | 181 | 27.49 |