title: A new approach for SSVEP detection using PARAFAC and canonical correlation analysis.
abstract: This paper presents a new way for automatic detection of SSVEPs through correlation analysis between tensor models. 3-way EEG tensor of channel × frequency × time is decomposed into constituting factor matrices using PARAFAC model. PARAFAC analysis of EEG tensor enables us to decompose multichannel EEG into constituting temporal, spectral and spatial signatures. SSVEPs characterized with localized spectral and spatial signatures are then detected exploiting a correlation analysis between extracted signatures of the EEG tensor and the corresponding simulated signatures of all target SSVEP signals. The SSVEP that has the highest correlation is selected as the intended target. Two flickers blinking at 8 and 13 Hz were used as visual stimuli and the detection was performed based on data packets of 1 second without overlapping. Five subjects participated in the experiments and the highest classification rate of 83.34% was achieved, leading to the Information Transfer Rate (ITR) of 21.01 bits/min.
authors.forename: Richard
authors.initials: R
authors.lastname: Tello
authors.forename: Saeed
authors.initials: S
authors.lastname: Pouryazdian
authors.forename: Andre
authors.initials: A
authors.lastname: Ferreira
authors.forename: Soosan
authors.initials: S
authors.lastname: Beheshti
authors.forename: Sridhar
authors.initials: S
authors.lastname: Krishnan
authors.forename: Teodiano
authors.initials: T
authors.lastname: Bastos
mesh_terms: D000328:Adult; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D033281:Nontherapeutic Human Experimentation; D010775:Photic Stimulation; D012815:Signal Processing, Computer-Assisted