6533b86efe1ef96bd12cbec9

RESEARCH PRODUCT

Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition

Fengyu CongYongjie ZhuTapani RistaniemiXueqiao Li

subject

source localizationoscillatorsBrain activity and meditationComputer scienceneural oscillationsPhysics::Medical Physics02 engineering and technologyElectroencephalographyTask (project management)03 medical and health sciencestensor decomposition0302 clinical medicineTensor (intrinsic definition)0202 electrical engineering electronic engineering information engineeringmedicineEEGTensorta515ta113Quantitative Biology::Neurons and Cognitionmedicine.diagnostic_testbusiness.industrybrain modelingPattern recognitionHuman brainoskillaattoritdata modelsElectrophysiologymedicine.anatomical_structuretask analysis020201 artificial intelligence & image processingArtificial intelligencebusinesstietomallitelectroencephalography030217 neurology & neurosurgery

description

The characterization of dynamic electrophysiological brain activity, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method with tensor decomposition for measuring the task-induced oscillations in the human brain using electroencephalography (EEG). The time frequency representation of source-reconstructed singletrail EEG data constructed a third-order tensor with three factors of time ∗ trails, frequency and source points. We then used a non-negative Canonical Polyadic decomposition (NCPD) to identify the temporal, spectral and spatial changes in electrophysiological brain activity. We validate this method using both simulation EEG data and real EEG data recorded during a task of irony comprehension. The results demonstrated that proposed method can track dynamics of the temporal-spectral modes of the rhythm in the brain on a timescale commensurate to the task they are undertaking. peerReviewed

yearjournalcountryeditionlanguage
2019-05-01ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
https://doi.org/10.1109/icassp.2019.8682355