Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition
| Autor: | Fengyu Cong, Yongjie Zhu, Tapani Ristaniemi, Xueqiao Li |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
source localization
oscillators Brain activity and meditation Computer science neural oscillations Physics::Medical Physics 02 engineering and technology Electroencephalography Task (project management) 03 medical and health sciences tensor decomposition 0302 clinical medicine Tensor (intrinsic definition) 0202 electrical engineering electronic engineering information engineering medicine EEG Tensor ta515 ta113 Quantitative Biology::Neurons and Cognition medicine.diagnostic_test business.industry brain modeling Pattern recognition Human brain oskillaattorit data models Electrophysiology medicine.anatomical_structure task analysis 020201 artificial intelligence & image processing Artificial intelligence business tietomallit electroencephalography 030217 neurology & neurosurgery |
| Zdroj: | ICASSP |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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