Search results for "EEG"
showing 10 items of 313 documents
Applying Hilbert-Huang transform to mismatch negativity
2009
Extraction of event-related potentials from electroencephalography data
2009
Alleviating Class Imbalance Problem in Automatic Sleep Stage Classification
2022
For real-world automatic sleep-stage classification tasks, various existing deep learning-based models are biased toward the majority with a high proportion. Because of the unique sleep structure, most of the current polysomnography (PSG) datasets suffer an inherent class imbalance problem (CIP), in which the number of each sleep stage is severely unequal. In this study, we first define the class imbalance factor (CIF) to describe the level of CIP quantitatively. Afterward, we propose two balancing methods to alleviate this problem from the dataset quantity and the relationship between the class distribution and the applied model, respectively. The first one is to employ the data augmentati…
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 …
Spatio-temporal Dynamical Analysis of Brain Activity during Mental Fatigue Process
2021
Mental fatigue is a common phenomenon with implicit and multidimensional properties. It brings dynamic changes of functional brain networks. However, the challenging problem of false positives appears when the connectivity is estimated by Electroencephalography (EEG). In this paper, we propose a novel framework based on spatial clustering to explore the sources of mental fatigue and functional activity changes caused by them. To suppress the false positive observations, spatial clustering is implemented in brain networks. The nodes extracted by spatial clustering are registered back to functional magnetic resonance imaging (fMRI) source space to determined the sources of mental fatigue. The…
Binaurālo pukstu ietekme uz EEG un stājas stabilitāti
2016
Pētījums attiecas uz aktuālu klīniskās fizioloģijas un neirozinātnes problēmu, kura ir saistīta ar dažādu eksogēnu stimulu spēju ietekmēt gan cilvēka smadzeņu darbību, gan stājas stabilitāti. Pētījums veikts ar mērķi noskaidrot binaurālo pukstu (BP) stimulācijas ietekmi uz pieaugušu jaunu cilvēku nomoda elektroencefalogrammu (EEG) un stājas stabilitātes parametriem, kā arī savstarpēji salīdzinoši novērtēt vairākus objektīvus un subjektīvus BP sensitivitātes kritērijus. Binaurālo pukstu tūlītējās, daudzbusīgās ietekmes kopaina ir īpatnēja katram indivīdam, līdz ar to – indivīda binaurālo pukstu jutības (sensitivitātes) vērtējumā ir jāizmanto vairāku fizioloģisko reakciju spilgtumu raksturojo…
Load-dependent alpha suppression is related to working memory capacity for numbers
2022
Alpha suppression is proposed to reflect a surge in cortical excitability to enhance stimulus processing in working memory. The attenuated state of alpha might reflect the prioritisation of behaviourally relevant information, making it a proxy for working memory functioning. Despite the growing interest in utilising the advancement of brain-based measures to evaluate individuals’ cognitive processes, there was a lack of consistent evidence on the relationship between alpha suppression and working memory performance. To investigate whether interindividual differences in alpha suppression might be related to variability in working memory capacity, we recorded participants’ electroencephalogra…
The prediction of three driving quality states of simulated driving by psychophysiological variables with sleep-deprived subjects
2005
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 …
Deriving electrophysiological brain network connectivity via tensor component analysis during freely listening to music
2020
Recent studies show that the dynamics of electrophysiological functional connectivity is attracting more and more interest since it is considered as a better representation of functional brain networks than static network analysis. It is believed that the dynamic electrophysiological brain networks with specific frequency modes, transiently form and dissolve to support ongoing cognitive function during continuous task performance. Here, we propose a novel method based on tensor component analysis (TCA), to characterize the spatial, temporal, and spectral signatures of dynamic electrophysiological brain networks in electroencephalography (EEG) data recorded during free music-listening. A thr…