Data selection in EEG signals classification

Wang, Shuaifang; Li, Yan; Wen, Peng; Lai, David

doi:10.1007/s13246-015-0414-x

Cited by 9 publications

(2 citation statements)

References 25 publications

(2 reference statements)

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“…Identification of (two) EEG channels with maximum activity is common [132][133][134], thus the presence of multiple (dipole) sources does not diminish our protocol to target one identified source. Evidently the scalp EEG measurements vary in time, but standard time-domain signal processing can be used to select channels [108,[135][136][137][138][139]. Ultimately, adoption of our approach to refine clinical treatments with tES/tDCS, depends on still further assumptions such as if targeted and individualized cortical stimulation of preferred over brain-wide stimulation.…”

Section: Discussionmentioning

confidence: 99%

A simple method for EEG guided transcranial electrical stimulation without models

Cancelli

Cottone²,

Tecchio³

et al. 2016

J. Neural Eng.

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Section: Discussionmentioning

confidence: 99%

A simple method for EEG guided transcranial electrical stimulation without models

Cancelli

Cottone²,

Tecchio³

et al. 2016

J. Neural Eng.

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“…Graph entropies have been applied efficiently for alcoholism EEG identification [14,15]. The graph entropy GE of the degree distribution p(i) is defined as follows:…”

Section: Graph Entropymentioning

confidence: 99%

Detecting Depression Using Single-Channel EEG and Graph Methods

et al. 2022

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Objective: This paper applies graph methods to distinguish major depression disorder (MDD) and healthy (H) subjects using the graph features of single-channel electroencephalogram (EEG) signals. Methods: Four network features—graph entropy, mean degree, degree two, and degree three—were extracted from the 19-channel EEG signals of 64 subjects (26 females and 38 males), and then these features were forwarded to a support vector machine to conduct depression classification based on the eyes-open and eyes-closed statuses, respectively. Results: Statistical analysis showed that graph features with degree of two and three, the graph entropy of MDD was significantly lower than that for H (p < 0.0001). Additionally, the accuracy of detecting MDD using single-channel T4 EEG with leave-one-out cross-validation from H was 89.2% and 92.0% for the eyes-open and eyes-closed statuses, respectively. Conclusion: This study shows that the graph features of a short-term EEG can help assess and evaluate MDD. Thus, single-channel EEG signals can be used to detect depression in subjects. Significance: Graph feature analysis discovered that MDD is more related to the temporal lobe than the frontal lobe.

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Development of single-channel electroencephalography signal analysis model for real-time drowsiness detection

Balam

Chinara

2021

Phys Eng Sci Med

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Data selection in EEG signals classification

Abstract: The alcoholism can be detected by 1 analyzing electroencephalogram (EEG) signals. 2

Cited by 9 publications

References 25 publications

A simple method for EEG guided transcranial electrical stimulation without models

A simple method for EEG guided transcranial electrical stimulation without models

Detecting Depression Using Single-Channel EEG and Graph Methods

Development of single-channel electroencephalography signal analysis model for real-time drowsiness detection

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