Search
Abstract
In this work we analysed the application of con-volutional neural networks in motor imagery classification for the Brain Computer Interface (BCI) purposes. To increase the accuracy of classification we proposed the solution that combines the Common Spatial Pattern (CSP) with convolutional network (ConvNet). The electroencephalography (EEG) is one of the modalities we try to use for controlling the prosthetic arm. Therefor in this paper we exploited the subject dependent approach and show results for models trained individually for a particular subject. Although the ConvNets are design to work directly with EEG data, presented approach of joining CSP with ConvNet shows increase in accuracy of movement classification. In average, our approach resulted in ∼80% accuracy.
Citations
-
0
CrossRef
-
0
Web of Science
-
1
Scopus
Authors (2)
-
Łukasz Radzinski mgr inż
Cite as
Full text
full text is not available in portal
Keywords
Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Radzinski Ł., Kocejko T.: Deep learning approach on surface EEG based Brain Computer Interface// / : , 2022,
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/hsi55341.2022.9869461
- Sources of funding:
-
- Statutory activity/subsidy
- Verified by:
- Gdańsk University of Technology
seen 107 times
Recommended for you
Transfer learning in imagined speech EEG-based BCIs
- J. Garcia Salinas,
- L. Villaseñor-Pineda,
- C. A. Reyes-Garćia
- + 1 authors
Tensor Decomposition for Imagined Speech Discrimination in EEG
- J. Garcia Salinas,
- L. Villaseñor-Pineda,
- C. A. Reyes-Garćia
- + 1 authors