EEG Based Detection of Alcoholics

Shri, T. K. Padma; Sriraam, N.

doi:10.4018/ijbce.2012010105

Cited by 9 publications

(1 citation statement)

References 65 publications

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“…The conventional techniques used for distinguishing the alcoholic VERPs from the controls primarily consist of 2 steps, developing a set of feature vectors and training a classifier using these feature vectors (Palaniappan, 2002(Palaniappan, , 2005(Palaniappan, , 2006(Palaniappan, , 2007Ong, 2005;Kousarrizi, 2009;Shri and Sriraam, 2012;Shahina, 2008). Most authors have used the information in the gamma band (30-50 Hz) to develop feature vectors.…”

Section: Discussionmentioning

confidence: 99%

New crossover operators for multiple subset selection tasks

Roy,

Schaffer,

Laramee

2014

Preprint

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We have introduced two crossover operators, MMX-BLX exploit and MMX-BLX explore , for simultaneously solving multiple feature/subset selection problems where the features may have numeric attributes and the subset sizes are not predefined. These operators differ on the level of exploration and exploitation they perform; one is designed to produce convergence controlled mutation and the other exhibits a quasiconstant mutation rate. We illustrate the characteristic of these operators by evolving pattern detectors to distinguish alcoholics from controls using their visually evoked response potentials (VERPs). This task encapsulates two groups of subset selection problems; choosing a subset of EEG leads along with the lead-weights (features with attributes) and the other that defines the temporal pattern that characterizes the alcoholic VERPs. We observed better generalization performance from MMX-BLX explore . Perhaps, MMX-BLX exploit was handicapped by not having a restart mechanism. These operators are novel and appears to hold promise for solving simultaneous feature selection problems.

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Section: Discussionmentioning

confidence: 99%

New crossover operators for multiple subset selection tasks

Roy,

Schaffer,

Laramee

2014

Preprint

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Examining dynamic functional relationships in a pathological brain using evolutionary computation

Roy

2017

Soft Comput

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Pattern recognition of spectral entropy features for detection of alcoholic and control visual ERP’s in multichannel EEGs

Shri

Sriraam

2017

Brain Inf.

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This paper presents a novel ranking method to select spectral entropy (SE) features that discriminate alcoholic and control visual event-related potentials (ERP'S) in gamma sub-band (30-55 Hz) derived from a 64-channel electroencephalogram (EEG) recording. The ranking is based on a t test statistic that rejects the null hypothesis that the group means of SE values in alcoholics and controls are identical. The SE features with high ranks are indicative of maximal separation between their group means. Various sizes of top ranked feature subsets are evaluated by applying principal component analysis (PCA) and k-nearest neighbor (k-NN) classification. Even though ranking does not influence the performance of classifier significantly with the selection of all 61 active channels, the classification efficiency is directly proportional to the number of principal components (pc). The effect of ranking and PCA on classification is predominantly observed with reduced feature subsets of (N = 25, 15) top ranked features. Results indicate that for N = 25, proposed ranking method improves the k-NN classification accuracy from 91 to 93.87% as the number of pcs increases from 5 to 25. With same number of pcs, the k-NN classifier responds with accuracies of 84.42-91.54% with non-ranked features.Similarly for N = 15 and number of pcs varying from 5 to 15, ranking enhances k-NN detection accuracies from 88.9 to 93.08% as compared to 86.75-91.96% without ranking. This shows that the detection accuracy is increased by 6.5 and 2.8%, respectively, for N = 25, whereas it enhances by 2.2 and 1%, respectively, for N = 15 in comparison with non-ranked features. In the proposed t test ranking method for feature selection, the pcs of only top ranked feature candidates take part in classification process and hence provide better generalization.

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EEG Based Detection of Alcoholics

Cited by 9 publications

References 65 publications

New crossover operators for multiple subset selection tasks

New crossover operators for multiple subset selection tasks

Examining dynamic functional relationships in a pathological brain using evolutionary computation

Pattern recognition of spectral entropy features for detection of alcoholic and control visual ERP’s in multichannel EEGs

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