Brain computer interface (BCI) could be useful in improving the quality of life for paralyzed patients. Motor imagery classification has recently been a center of research interest in the BCI-based rehabilitation. As of current, spatial features and spectral features were often used independently for motor imagery classification. While few studies attempted to combine the information from varying domains including spectral, spatial and temporal feature, the attempts employed simplistic linear models. In this study, a novel feature extraction method for including spatial and temporal information is proposed. The method uses recurrent convolutional neural network (RCNN) which excels in temporal and spatial classification. The method was tested for classifying wrist twisting-related task classification during manipulation of robotic arm via electroencephalography, and the performance of the method was compared to the conventional motor imagery classifiers with common spatial pattern (CSP) filter. The proposed method showed 73.9% accuracy in the classification of three types of tasks, whereas the highest accuracy achieved by conventional models was 59.5%. Overall, the performance of the proposed RCNN model was greater than the conventional models using the CSP as input features. The findings warrant further application of the proposed methods in varying BCI environment.
|Title of host publication||7th International Winter Conference on Brain-Computer Interface, BCI 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2019 Feb|
|Event||7th International Winter Conference on Brain-Computer Interface, BCI 2019 - Gangwon, Korea, Republic of|
Duration: 2019 Feb 18 → 2019 Feb 20
|Name||7th International Winter Conference on Brain-Computer Interface, BCI 2019|
|Conference||7th International Winter Conference on Brain-Computer Interface, BCI 2019|
|Country/Territory||Korea, Republic of|
|Period||19/2/18 → 19/2/20|
Bibliographical noteFunding Information:
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No. 2017-0-00432, Development of non-invasive integrated BCI SW platform to control home appliances and external devices by user's thought via AR/VR interface).
© 2019 IEEE.
- deep learning
- motor imagery
- recurrent convolutional neural network
- robot arm
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Signal Processing
- Neuroscience (miscellaneous)