Simulation, Modification and Dimension Reduction of EEG Feature Space

Piorecký, Marek; Černá, Eva; Piorecka, Vaclava; Krajča, Vladimı́r; Koudelka, Vlastimil

doi:10.1007/978-981-10-9038-7_80

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…With regard to the size of the dimension—for one subject, 256 vectors (corresponds to the number of channels)—an appropriate method for reducing the dimension of selected electrodes was chosen. In previous papers, several linear [ 36 , 37 ] and non-linear [ 38 , 39 , 40 ] dimension reduction methods were utilised directly to EEG data. Nevertheless, it is still unknown what combination of EEG features and the dimension reduction technique reliably describes the BOLD signal and if the association is linear or not.…”

Section: Methodsmentioning

confidence: 99%

Simultaneous fMRI-EEG-Based Characterisation of NREM Parasomnia Disease: Methods and Limitations

et al. 2020

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Functional magnetic resonance imaging (fMRI) techniques and electroencephalography (EEG) were used to investigate sleep with a focus on impaired arousal mechanisms in disorders of arousal (DOAs). With a prevalence of 2–4% in adults, DOAs are significant disorders that are currently gaining attention among physicians. The paper describes a simultaneous EEG and fMRI experiment conducted in adult individuals with DOAs (n=10). Both EEG and fMRI data were validated by reproducing well established EEG and fMRI associations. A method for identification of both brain functional areas and EEG rhythms associated with DOAs in shallow sleep was designed. Significant differences between patients and controls were found in delta, theta, and alpha bands during awakening epochs. General linear models of the blood-oxygen-level-dependent signal have shown the secondary visual cortex and dorsal posterior cingulate cortex to be associated with alpha spectral power fluctuations, and the precuneus with delta spectral power fluctuations, specifically in patients and not in controls. Future EEG–fMRI sleep studies should also consider subject comfort as an important aspect in the experimental design.

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Section: Methodsmentioning

confidence: 99%

Simultaneous fMRI-EEG-Based Characterisation of NREM Parasomnia Disease: Methods and Limitations

et al. 2020

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“…The labels are made optically in this data -sets are visually separable. We assumed that the EEG space contains nested clusters [17], therefore, we tested the ability of algorithms to separate such spatial clusters. Training sets demonstrate the disadvantages of k-means classification and their compensation using DBSCAN and DENCLUE methods.…”

Section: Datamentioning

confidence: 99%

Automatic Eeg Classification Using Density Based Algorithms Dbscan and Denclue

2019

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Electroencephalograph (EEG) is a commonly used method in neurological practice. Automatic classifiers (algorithms) highlight signal sections with interesting activity and assist an expert with record scoring. Algorithm K-means is one of the most commonly used methods for EEG inspection. In this paper, we propose/apply a method based on density-oriented algorithms DBSCAN and DENCLUE. DBSCAN and DENCLUE separate the nested clusters against K-means. All three algorithms were validated on a testing dataset and after that adapted for a real EEG records classification. 24 dimensions EEG feature space were classified into 5 classes (physiological, epileptic, EOG, electrode, and EMG artefact). Modified DBSCAN and DENCLUE create more than two homogeneous classes of the epileptic EEG data. The results offer an opportunity for the EEG scoring in clinical practice. The big advantage of the proposed algorithms is the high homogeneity of the epileptic class.

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Simulation, Modification and Dimension Reduction of EEG Feature Space

Cited by 2 publications

References 8 publications

Simultaneous fMRI-EEG-Based Characterisation of NREM Parasomnia Disease: Methods and Limitations

Simultaneous fMRI-EEG-Based Characterisation of NREM Parasomnia Disease: Methods and Limitations

Automatic Eeg Classification Using Density Based Algorithms Dbscan and Denclue

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