In this paper, we report on preliminary results of research whose aim is to classify EEG signals recorded from a subject whilst controlling a joystick and moving it io different directions. We develop a method based on extracting salient spatio-temporal features from the EEG signals using continuous wavelet transform. We perform principal component analysis on these features as means to assess their usefulness for classification and to reduce the dimensionality of the problem. We use the results from the PCA as inputs to a neural network based classifier. The classification results show that we are able to discriminate between different directions using the selected features. This shows that this approach could be potentially useful in building braincomputer interfaces (BCIs) where a paralysed person could communicate with a wheelchair and steer it to the desired direction using only EEG signals.
|Number of pages||3|
|Journal||The IEE International Workshop on Intelligent Environments|
|Publication status||Published - 29 Jun 2005|
- EEG signals
- salient spatio-temporal features
- continuous wavelet transform
- neural network based classifier