Search results for "BRAIN ACTIVITY"

showing 3 items of 113 documents

Effects of Transcranial Direct Current Stimulation on Baseline and Slope of Prefrontal Cortex Hemodynamics During a Spatial Working Memory Task

2020

Background: Transcranial direct current stimulation (tDCS) has been shown to be an inexpensive, safe, and effective way of augmenting a variety of cognitive abilities. Relatively recent advances in neuroimaging technology have provided the ability to measure brain activity concurrently during active brain stimulation rather than after stimulation. The effects on brain activity elicited by tDCS during active tDCS reported by initial studies have been somewhat conflicted and seemingly dependent on whether a behavioral improvement was observed. Objective: The current study set out to address questions regarding behavioral change, within and between-participant designs as well as differentiatin…

mixed modelsBrain activity and meditationmedicine.medical_treatmentfNIRSSpatial memory050105 experimental psychologytDCSworking memorylcsh:RC321-57103 medical and health sciencesBehavioral Neuroscience0302 clinical medicineMedicine0501 psychology and cognitive sciencesPrefrontal cortexlcsh:Neurosciences. Biological psychiatry. NeuropsychiatryBiological PsychiatryOriginal ResearchTranscranial direct-current stimulationbusiness.industryWorking memory05 social sciencesHuman NeuroscienceDorsolateral prefrontal cortexPsychiatry and Mental healthNeuropsychology and Physiological Psychologymedicine.anatomical_structureNeurologyBrain stimulationFunctional near-infrared spectroscopybusinessneural efficiencyNeuroscience030217 neurology & neurosurgeryFrontiers in Human Neuroscience
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Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition

2019

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 …

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 & neurosurgeryICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Exploiting ongoing EEG with multilinear partial least squares during free-listening to music

2016

During real-world experiences, determining the stimulus-relevant brain activity is excitingly attractive and is very challenging, particularly in electroencephalography. Here, spectrograms of ongoing electroencephalogram (EEG) of one participant constructed a third-order tensor with three factors of time, frequency and space; and the stimulus data consisting of acoustical features derived from the naturalistic and continuous music formulated a matrix with two factors of time and the number of features. Thus, the multilinear partial least squares (PLS) conforming to the canonical polyadic (CP) model was performed on the tensor and the matrix for decomposing the ongoing EEG. Consequently, we …

ta113Multilinear mapmedicine.diagnostic_testBrain activity and meditationSpeech recognition02 engineering and technologyElectroencephalographyta3112Matrix decomposition03 medical and health sciences0302 clinical medicinetensor decompositionFrequency domainPartial least squares regression0202 electrical engineering electronic engineering information engineeringmedicineSpectrogramOngoing EEG020201 artificial intelligence & image processingmusicTime domain030217 neurology & neurosurgerymultilinear partial least squaresMathematics
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