Abstract
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.
Original language | English |
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Pages (from-to) | 199-202 |
Number of pages | 3 |
Journal | The IEE International Workshop on Intelligent Environments |
Publication status | Published - 29 Jun 2005 |
Keywords
- EEG signals
- salient spatio-temporal features
- continuous wavelet transform
- neural network based classifier
- braincomputer