TY - GEN
T1 - Spatiotemporal source tuning filter bank for multiclass EEG based brain computer interfaces
AU - Acharya, Soumyadipta
AU - Mollazadeh, Mohsen
AU - Murari, Kartikeya
AU - Thakor, Nitish
PY - 2006/12/1
Y1 - 2006/12/1
N2 - Non invasive brain-computer interfaces (BCI) allow people to communicate by modulating features of their electroencephalogram (EEG). Spatiotemporal filtering has a vital role in multi-class, EEG based BCI. In this study, we used a novel combination of Principle Component Analysis, Independent Component Analysis and Dipole Source Localization to design a Spatiotemporal Multiple Source Tuning (SPAMSORT) filter bank, each channel of which was tuned to the activity of an underlying dipole source. Changes in the Event-Related Spectral Perturbation (ERSP) were measured and used to train a linear Support Vector Machine to classify between four classes of motor imagery tasks (left hand, right hand, foot and tongue) for one subject. ERSP values were significantly (p<0.01) different across tasks and better (p<0.01) than conventional spatial filtering methods (Large Laplacian and Common Average Reference). Classification resulted in an average accuracy of 82.5%. This approach could lead to promising BCI applications such as control of a prosthesis with multiple degrees of freedom.
AB - Non invasive brain-computer interfaces (BCI) allow people to communicate by modulating features of their electroencephalogram (EEG). Spatiotemporal filtering has a vital role in multi-class, EEG based BCI. In this study, we used a novel combination of Principle Component Analysis, Independent Component Analysis and Dipole Source Localization to design a Spatiotemporal Multiple Source Tuning (SPAMSORT) filter bank, each channel of which was tuned to the activity of an underlying dipole source. Changes in the Event-Related Spectral Perturbation (ERSP) were measured and used to train a linear Support Vector Machine to classify between four classes of motor imagery tasks (left hand, right hand, foot and tongue) for one subject. ERSP values were significantly (p<0.01) different across tasks and better (p<0.01) than conventional spatial filtering methods (Large Laplacian and Common Average Reference). Classification resulted in an average accuracy of 82.5%. This approach could lead to promising BCI applications such as control of a prosthesis with multiple degrees of freedom.
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U2 - 10.1109/IEMBS.2006.259436
DO - 10.1109/IEMBS.2006.259436
M3 - Conference contribution
C2 - 17946815
AN - SCOPUS:34047095445
SN - 1424400325
SN - 9781424400324
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SP - 327
EP - 330
BT - 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
T2 - 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Y2 - 30 August 2006 through 3 September 2006
ER -