TY - GEN
T1 - Spatio-temporal modeling for dense array ERP classification
AU - Kota, Srinivas
AU - Gupta, Lalit
AU - Molfese, Dennis
AU - Vaidyanathan, Ravi
PY - 2008
Y1 - 2008
N2 - A new strategy is introduced to exploit the enhanced spatial resolution offered by dense electrode arrays and to solve the dimensionality problem that plagues the design and evaluation of practical dense array event-related potential (ERP) classifiers. A spatiotemporal model is introduced to observe the dense array ERP amplitude variations across channels and time, simultaneously. Dimensionality reduction is achieved by selecting elements of the spatio-temporal arrays which differ in their probability distributions across the brain activity classes. Each selected spatio-temporal element is classified using an univariate Gaussian classifier and the resulting decisions are fused into a decision fusion vector which is classified using a discrete Bayes vector classifier. Using ERPs from a Stroop color test, it is shown that the performance improves significantly when the strategy is applied to normalized spatio-temporal ERP arrays. The main advantage of the new strategy is that it is not constrained by the dimensionality of the ERP vector. Consequently, it can be used to design ERP classifiers specialized for individual test subjects without having to collect a large number of ERPs from groups of subjects in order to solve the dimensionality problem.
AB - A new strategy is introduced to exploit the enhanced spatial resolution offered by dense electrode arrays and to solve the dimensionality problem that plagues the design and evaluation of practical dense array event-related potential (ERP) classifiers. A spatiotemporal model is introduced to observe the dense array ERP amplitude variations across channels and time, simultaneously. Dimensionality reduction is achieved by selecting elements of the spatio-temporal arrays which differ in their probability distributions across the brain activity classes. Each selected spatio-temporal element is classified using an univariate Gaussian classifier and the resulting decisions are fused into a decision fusion vector which is classified using a discrete Bayes vector classifier. Using ERPs from a Stroop color test, it is shown that the performance improves significantly when the strategy is applied to normalized spatio-temporal ERP arrays. The main advantage of the new strategy is that it is not constrained by the dimensionality of the ERP vector. Consequently, it can be used to design ERP classifiers specialized for individual test subjects without having to collect a large number of ERPs from groups of subjects in order to solve the dimensionality problem.
KW - Decision fusion
KW - Dense arrays
KW - Dimensionality reduction
KW - Event-related potentials
KW - Spatio-temporal modeling
UR - http://www.scopus.com/inward/record.url?scp=61849161262&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=61849161262&partnerID=8YFLogxK
U2 - 10.1109/iembs.2008.4649605
DO - 10.1109/iembs.2008.4649605
M3 - Conference contribution
C2 - 19163108
AN - SCOPUS:61849161262
SN - 9781424418152
T3 - Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"
SP - 2091
EP - 2094
BT - Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
PB - IEEE Computer Society
T2 - 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
Y2 - 20 August 2008 through 25 August 2008
ER -