METHODS: Stimuli were presented in both monocular and dichoptic conditions at eight visual field locations/eye. The incommensurate stimulus frequencies ranged from 15.45 to 21.51 Hz. Five stimulus conditions differing in spatial frequency and orientation were examined for three viewing conditions. The resulting 15 stimulus conditions were examined in 16 normal subjects who repeated all conditions twice.
RESULTS: Several significant independent effects were identified. Response amplitudes were reduced for dichoptic viewing (by 0.85 times, p<4 x 10(-11)); offset by increases in responses for between eye differences of one octave of spatial frequency: lower (1.15 times, 0.1 cpd); higher (1.29 times, 0.4 cpd), both p<1.8 x 10(-7). Crossed orientations produced significant effects upon response phase (p=0.023) but not amplitude (p=0.062).
CONCLUSIONS: The results indicated that dichoptic evoked potentials using multifocal frequency-doubling illusion stimuli are practical. The use of crossed orientation, or differing spatial frequencies, in the two eyes reduced binocular interactions.
SIGNIFICANCE: The results indicate a method wherein several spatial or temporal and frequencies per visual field region can be tested in reasonable time using a multifocal VEP using spatial frequency-doubling stimuli.
METHODS: In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes.
RESULTS: Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature.
CONCLUSIONS: The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.