Alcoholism-related alterations in spectrum, coherence, and phase synchrony of topical electroencephalogram

Tcheslavski, Gleb V.; Gonen, Fahrettin F.

doi:10.1016/j.compbiomed.2011.12.006

Cited by 54 publications

(34 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, no answer was needed for single stimulation, but the participants under double stimulations were required to judge whether the two figures were the same and press buttons to reply. More specific details about the participants and data description are shown in [17]. In this study, the data of 28 alcoholics and 28 healthy volunteers under three experimental conditions (S1, S2 Match, and S2 No Match) for eight seconds (2048 time points in total) were chosen for further analysis, since the data was incomplete due to lack of concentration and wrong replies given by volunteers.…”

Section: Datamentioning

confidence: 99%

Disturbed Connectivity of EEG Functional Networks in Alcoholism: A Graph-Theoretic Analysis

Cao

et al. 2014

Bio-Medical Materials and Engineering

View full text Add to dashboard Cite

Generally, an alcoholic's brain shows explicit damage. However, in cognitive tasks, the correlation between the topological structural changes of the brain networks and the brain damage is still unclear. Scalp electrodes and synchronization likelihood (SL) were applied to the constructions of the EGG functional networks of 28 alcoholics and 28 healthy volunteers. The graph-theoretic analysis showed that in cognitive tasks, compared with the healthy control group, the brain networks of alcoholics had smaller clustering coefficients in β1 bands, shorter characteristic path lengths, increased global efficiency, but similar small-world properties. The abnormal topological structure of the alcoholics may be related to the local-function brain damage and the compensation mechanism adopted to complete tasks. This conclusion provides a new perspective for alcoholrelated brain damage.

show abstract

Section: Datamentioning

confidence: 99%

Disturbed Connectivity of EEG Functional Networks in Alcoholism: A Graph-Theoretic Analysis

Cao

et al. 2014

Bio-Medical Materials and Engineering

View full text Add to dashboard Cite

show abstract

“…They reported that Burg's method produced the most significant features for classification. Tcheslavski and Gonen [101] observed low phase synchrony in alpha and beta rhythms of EEG signals from PWA using spectrum, coherence, and phase synchrony features.…”

Section: Authorsmentioning

confidence: 98%

“…• Area under the ROC curve = 0.822 Tcheslavski and Gonen [101] Spectrum, coherence, and phase synchrony Statistical analysis • Lower phase synchrony at alpha and beta rhythm is observed in the alcoholism-related EEG signals Acharya et al [102] ApEn, SampEn, LLE, HOS SVM, 7…”

Section: Conflict Of Interestmentioning

confidence: 99%

Computer-aided diagnosis of alcoholism-related EEG signals

Acharya

Sudevan

Bhat

et al. 2014

Epilepsy & Behavior

View full text Add to dashboard Cite

“…E. Malar et al [10] used EEG power spectral density to identify drunk drivers. Power and coherence of various EEG rhythms are an important distinguishing factors for alcoholic and non-alcoholic subjects [11]. Coherence, which measures a linear correlation between signals, can also be used to distinguish between alcoholic EEG and non-alcoholic EEG [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid features based classification of alcoholic and non-alcoholic EEG

Sinha

2015

2015 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

View full text Add to dashboard Cite

Electroencephalography signals, which are a measure of brain functioning, can be used to distinguish between alcoholic and non-alcoholic due to adverse effect of alcohol on the brain. In the paper, proposed is three test scenarios carried out with different classifiers. The first case involves use of raw data as the feature set and in the second case, derived features after wavelet decomposition like energy, entropy, interquartile range and median absolute deviation of each sub band are utilized. The final case involves the use of both the raw data and the derived features called the hybrid feature set for classification. It was found that the use of hybrid feature set as the input to k-Nearest Neighbor and fuzzy k-NN on a dataset containing 70 subject (each with 10 trials) comprising of 35 controls and 35 alcoholic provided a peak classification accuracy of 88% within theta band in comparison to other test scenarios. Hence, use of hybrid features seems to be a promising approach to classification of EEG as alcoholic and non-alcoholic.CONECCT 2015 1570128025 1 978-1-4799-9985-9/15/$31.00 ©2015 IEEE

show abstract

Alcoholism-related alterations in spectrum, coherence, and phase synchrony of topical electroencephalogram

Cited by 54 publications

References 46 publications

Disturbed Connectivity of EEG Functional Networks in Alcoholism: A Graph-Theoretic Analysis

Disturbed Connectivity of EEG Functional Networks in Alcoholism: A Graph-Theoretic Analysis

Computer-aided diagnosis of alcoholism-related EEG signals

Hybrid features based classification of alcoholic and non-alcoholic EEG

Contact Info

Product

Resources

About