title: Reliable identification of mental tasks using time-embedded EEG and sequential evidence accumulation.
abstract: Eleven channels of EEG were recorded from a subject performing four mental tasks. A time-embedded representation of the untransformed EEG samples was constructed. Classification of the time-embedded samples was performed by linear and quadratic discriminant analysis and by an artificial neural network. A classifier's output for consecutive samples is combined to increase reliability. A new performance measure is defined as the number of correct selections that would be made by a brain-computer interface (BCI) user of the system, accounting for the need for an incorrect selection to be followed by a correct one to 'delete' the previous selection. A best result of 0.32 correct selections s(-1) (about 3 s per BCI decision) was obtained with a neural network using a time-embedding dimension of 50.
authors: Department of Computer Science, Colorado State University, Fort Collins, CO, USA.
authors: Charles
authors: C
authors: Anderson
authors: Elliott
authors: E
authors: Forney
authors: Douglas
authors: D
authors: Hains
authors: Annamalai
authors: A
authors: Natarajan
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D014584:User-Computer Interface