title: Learning discrimination trajectories in EEG sensor space: application to inferring task difficulty.
abstract: We describe a spatio-temporal linear discriminator for single-trial classification of multi-channel electroencephalography (EEG). No prior information about the characteristics of the neural activity is required, i.e., the algorithm requires no knowledge about the timing and spatial distribution of the evoked responses. The algorithm finds a temporal delay/window onset time for each EEG channel and then spatially integrates the channels for each channel-specific onset time. The algorithm can be seen as learning discrimination trajectories defined within the space of EEG channels. We demonstrate the method for detecting auditory-evoked neural activity and discrimination of task difficulty in a complex visual-auditory environment.
authors.affiliation: Department of Biomedical Engineering, Columbia University, New York City, NY, USA. al2082@columbia.edu
authors.forename: An
authors.initials: A
authors.lastname: Luo
authors.forename: Paul
authors.initials: P
authors.lastname: Sajda
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016002:Discriminant Analysis; D004193:Discrimination Learning; D004569:Electroencephalography / Q000379:methods*; D005072:Evoked Potentials, Auditory / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D013647:Task Performance and Analysis