Single-trial classification of vowel speech imagery using common spatial patterns Export

@article{citeulike:6055432, abstract = {With the goal of providing a speech prosthesis for individuals with severe communication impairments, we propose a control scheme for brain–computer interfaces using vowel speech imagery. Electroencephalography was recorded in three healthy subjects for three tasks, imaginary speech of the English vowels /a/ and /u/, and a no action state as control. Trial averages revealed readiness potentials at 200 ms after stimulus and speech related potentials peaking after 350 ms. Spatial filters optimized for task discrimination were designed using the common spatial patterns method, and the resultant feature vectors were classified using a nonlinear support vector machine. Overall classification accuracies ranged from 68\% to 78\%. Results indicate significant potential for the use of vowel speech imagery as a speech prosthesis controller.}, author = {DaSalla, Charles S. and Kambara, Hiroyuki and Sato, Makoto and Koike, Yasuharu}, citeulike-article-id = {6055432}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.neunet.2009.05.008}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0893608009000999}, doi = {10.1016/j.neunet.2009.05.008}, issn = {08936080}, journal = {Neural Networks}, keywords = {aac, bci}, month = {November}, number = {9}, pages = {1334--1339}, posted-at = {2009-11-02 06:43:57}, priority = {2}, title = {Single-trial classification of vowel speech imagery using common spatial patterns}, url = {http://dx.doi.org/10.1016/j.neunet.2009.05.008}, volume = {22}, year = {2009} }

TY - JOUR ID - citeulike:6055432 L3 - citeulike-article-id:6055432 N2 - With the goal of providing a speech prosthesis for individuals with severe communication impairments, we propose a control scheme for brain–computer interfaces using vowel speech imagery. Electroencephalography was recorded in three healthy subjects for three tasks, imaginary speech of the English vowels /a/ and /u/, and a no action state as control. Trial averages revealed readiness potentials at 200 ms after stimulus and speech related potentials peaking after 350 ms. Spatial filters optimized for task discrimination were designed using the common spatial patterns method, and the resultant feature vectors were classified using a nonlinear support vector machine. Overall classification accuracies ranged from 68% to 78%. Results indicate significant potential for the use of vowel speech imagery as a speech prosthesis controller. IS - 9 JF - Neural Networks SN - 08936080 EP - 1339 TI - Single-trial classification of vowel speech imagery using common spatial patterns VL - 22 SP - 1334 KW - aac KW - bci AU - DaSalla, Charles AU - Kambara, Hiroyuki AU - Sato, Makoto AU - Koike, Yasuharu PY - 2009/11// UR - http://dx.doi.org/10.1016/j.neunet.2009.05.008 ER -