Singular values as a detector of epileptic seizures in EEG signals

Shahid, Arslan; Kamel, Nidal; Malik, Aamir Saeed

doi:10.1109/icias.2014.6869459

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Applications of using SVD to decompose multi-channel EEG signals have been reported theoretically and experimentally by previous studies. For example, Shahid et al applied SVD on EEG signals to identify epileptic seizures ( Shahid et al, 2014 ); Haddad et al proved the feasibility of using SVD to resolve EEG independent networks ( Haddad and Najafizadeh, 2015 ); Jonmohamadi et al reported the fused component using EEG and fMRI data with the help of SVD ( Jonmohamadi et al, 2019 ). To the best of our knowledge, no study has examined SVD as an analytical tool to resolve EEG networks and/or quantify tPBM-induced stimulation effects on the respective networks.…”

Section: Introductionmentioning

confidence: 99%

Combination of Group Singular Value Decomposition and eLORETA Identifies Human EEG Networks and Responses to Transcranial Photobiomodulation

Wang

Wanniarachchi

et al. 2022

Front. Hum. Neurosci.

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Transcranial Photobiomodulation (tPBM) has demonstrated its ability to alter electrophysiological activity in the human brain. However, it is unclear how tPBM modulates brain electroencephalogram (EEG) networks and is related to human cognition. In this study, we recorded 64-channel EEG from 44 healthy humans before, during, and after 8-min, right-forehead, 1,064-nm tPBM or sham stimulation with an irradiance of 257 mW/cm2. In data processing, a novel methodology by combining group singular value decomposition (gSVD) with the exact low-resolution brain electromagnetic tomography (eLORETA) was implemented and performed on the 64-channel noise-free EEG time series. The gSVD+eLORETA algorithm produced 11 gSVD-derived principal components (PCs) projected in the 2D sensor and 3D source domain/space. These 11 PCs took more than 70% weight of the entire EEG signals and were justified as 11 EEG brain networks. Finally, baseline-normalized power changes of each EEG brain network in each EEG frequency band (delta, theta, alpha, beta and gamma) were quantified during the first 4-min, second 4-min, and post tPBM/sham periods, followed by comparisons of frequency-specific power changes between tPBM and sham conditions. Our results showed that tPBM-induced increases in alpha powers occurred at default mode network, executive control network, frontal parietal network and lateral visual network. Moreover, the ability to decompose EEG signals into individual, independent brain networks facilitated to better visualize significant decreases in gamma power by tPBM. Many similarities were found between the cortical locations of SVD-revealed EEG networks and fMRI-identified resting-state networks. This consistency may shed light on mechanistic associations between tPBM-modulated brain networks and improved cognition outcomes.

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Section: Introductionmentioning

confidence: 99%

Combination of Group Singular Value Decomposition and eLORETA Identifies Human EEG Networks and Responses to Transcranial Photobiomodulation

Wang

Wanniarachchi

et al. 2022

Front. Hum. Neurosci.

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“…Singular value decomposition: Another important feature that we extracted was the singular value decomposition of the energy of the signal as a matrix [17].The Independent Component analysis Breakdown (SVD) of a matrix is a factoring of that matrix into matrices in linear algebra. It possesses some intriguing algebraic features and conveys key geometrical and analytical insights into pseudo signals such as the EEG [32]. It also has some crucial data analytics applications that will aid us in classifying in the future [57].The overall variation in A must be equivalent to the total of the square of the absolute values.…”

Section: Local Maximamentioning

confidence: 99%

Prediction And Detection In Change Of Cognitive Load For VIP's By A Machine Learning Approach

Rahman

Ahmed

Saad

et al. 2021

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

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“…3 EEG is a field of study that is prominently deployed to diagnose various conscious and subconscious brain activities. 4 Nevertheless, the detection of seizures from a visual inspection of the EEG scan can be challenging even for a trained neurologist for a variety of reasons, such as excessive presence of myogenic artifacts, 5 power frequency coupling, etc. Moreover, occurrence of these spikes is recorded during the time of seizures.…”

Section: Introductionmentioning

confidence: 99%