Abstract
| Language | English |
|---|---|
| Pages | 5404-5407 |
| Number of pages | 3 |
| DOIs | |
| Publication status | Published - 2005 |
| Event | 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Shanghai, China Duration: 1 Sep 2005 → 4 Sep 2005 |
Conference
| Conference | 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
|---|---|
| Abbreviated title | IEEE-EMBS 2005 |
| Country | China |
| City | Shanghai |
| Period | 1/09/05 → 4/09/05 |
Fingerprint
Keywords
- eeg classification
- movement
- displacement
- direction
Cite this
}
EEG classification based on movement direction and displacement. / Lakany, H.; Worrajiran, Ponpisut; Valsan, Gopal; Conway, B.A.
2005. 5404-5407 Paper presented at 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, China.Research output: Contribution to conference › Paper
TY - CONF
T1 - EEG classification based on movement direction and displacement
AU - Lakany, H.
AU - Worrajiran, Ponpisut
AU - Valsan, Gopal
AU - Conway, B.A.
N1 - Also presented at Neuroscience 2005 annual meeting.
PY - 2005
Y1 - 2005
N2 - Our aim is to assess and evaluate signal processing and classification methods for extracting features from EEG signals that are useful in developing brain-computer interfaces. In this paper, we report on results of developing a method to classify wrist movements using EEG signals recorded from a subject whilst controlling a joystick and moving it in different directions. Such method could be potentially useful in building brain-computer interfaces (BCIs) where a paralysed person could communicate with a wheelchair and steer it to the desired direction using only EEG signals. Our method is 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 means to represent the different directions. We use a simple technique based on Euclidean distance to classify the data. The classification results show that we are able to discriminate between different directions using the selected features
AB - Our aim is to assess and evaluate signal processing and classification methods for extracting features from EEG signals that are useful in developing brain-computer interfaces. In this paper, we report on results of developing a method to classify wrist movements using EEG signals recorded from a subject whilst controlling a joystick and moving it in different directions. Such method could be potentially useful in building brain-computer interfaces (BCIs) where a paralysed person could communicate with a wheelchair and steer it to the desired direction using only EEG signals. Our method is 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 means to represent the different directions. We use a simple technique based on Euclidean distance to classify the data. The classification results show that we are able to discriminate between different directions using the selected features
KW - eeg classification
KW - movement
KW - displacement
KW - direction
UR - http://dx.doi.org/10.1109/IEMBS.2005.1615704
U2 - 10.1109/IEMBS.2005.1615704
DO - 10.1109/IEMBS.2005.1615704
M3 - Paper
SP - 5404
EP - 5407
ER -