Robust classification of EEG signal for brain-computer interface Export

@article{Guan2006brain, abstract = {We report the implementation of a text input application (speller) based on the P300 event related potential. We obtain high accuracies by using an SVM classifier and a novel feature. These techniques enable us to maintain fast performance without sacrificing the accuracy, thus making the speller usable in an online mode. In order to further improve the usability, we perform various studies on the data with a view to minimizing the training time required. We present data collected from nine healthy subjects, along with the high accuracies (of the order of 95\% or more) measured online. We show that the training time can be further reduced by a factor of two from its current value of about 20 min. High accuracy, fast learning, and online performance make this P300 speller a potential communication tool for severely disabled individuals, who have lost all other means of communication and are otherwise cut off from the world, provided their disability does not interfere with the performance of the speller.}, author = {Thulasidas, M. and Guan, Cuntai and Wu, Jiankang}, booktitle = {Neural Systems and Rehabilitation Engineering, IEEE Transactions on}, citeulike-article-id = {3577438}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TNSRE.2005.862695}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1605260}, doi = {10.1109/TNSRE.2005.862695}, journal = {Neural Systems and Rehabilitation Engineering, IEEE Transactions on}, keywords = {brain, computer, eeg, interface}, number = {1}, pages = {24--29}, posted-at = {2008-11-19 07:19:33}, priority = {2}, title = {Robust classification of EEG signal for brain-computer interface}, url = {http://dx.doi.org/10.1109/TNSRE.2005.862695}, volume = {14}, year = {2006} }

TY - JOUR ID - Guan2006brain L3 - citeulike-article-id:3577438 N2 - We report the implementation of a text input application (speller) based on the P300 event related potential. We obtain high accuracies by using an SVM classifier and a novel feature. These techniques enable us to maintain fast performance without sacrificing the accuracy, thus making the speller usable in an online mode. In order to further improve the usability, we perform various studies on the data with a view to minimizing the training time required. We present data collected from nine healthy subjects, along with the high accuracies (of the order of 95% or more) measured online. We show that the training time can be further reduced by a factor of two from its current value of about 20 min. High accuracy, fast learning, and online performance make this P300 speller a potential communication tool for severely disabled individuals, who have lost all other means of communication and are otherwise cut off from the world, provided their disability does not interfere with the performance of the speller. IS - 1 JF - Neural Systems and Rehabilitation Engineering, IEEE Transactions on EP - 29 TI - Robust classification of EEG signal for brain-computer interface VL - 14 T2 - Neural Systems and Rehabilitation Engineering, IEEE Transactions on SP - 24 KW - brain KW - computer KW - eeg KW - interface AU - Thulasidas, M AU - Guan, Cuntai AU - Wu, Jiankang PY - 2006/// UR - http://dx.doi.org/10.1109/TNSRE.2005.862695 ER -