Mental and Behavioural Disorders Related to Alcohol and their Effects on EEG Signals – An Overview

Janvale, Ganesh B.; Kendre, Sanjeev; Mehrotra, S. C.

doi:10.1016/j.sbspro.2014.04.175

Cited by 10 publications

(2 citation statements)

References 15 publications

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“…Alcoholism is a disease caused by the habitual consumption of alcohol. Neurologists substantiate that alcoholism is the cause of neurological changes, which are responsible for mental and behavioural disorders [1]. The regular consumption of alcohol not only damages the organs of human body system, but it may also affect body's genes.…”

Section: Introductionmentioning

confidence: 99%

Rhythm‐based identification of alcohol EEG signals

Taran

Bajaj

2018

IET Science, Measurement & Technology

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Section: Introductionmentioning

confidence: 99%

Rhythm‐based identification of alcohol EEG signals

Taran

Bajaj

2018

IET Science, Measurement & Technology

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“…The four separate classes of dataset have been created according to four different frequency components (delta, theta, alpha, and beta) including all different modes (relaxation, calculation, counting, and reminding modes). The linear discriminant analysis (Fishers Algorithm) [7] has been implemented on class-within class matrix datasets. The aim of LDA is to use hyper-planes to separate the data representing the different classes.…”

Section: Analysis and Classificationmentioning

confidence: 99%

Analysis and Classification of EEG Signals Related to Arithmetic Operations

Janvale¹,

Gawali²,

Deshmukh³

et al. 2016

IJCA

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The advancement in computer hardware and digital signal processing made possible use of brain signal for the communication between human and computers. The electroencephalogram contains the information of brain signals. This paper focuses on mental tasks classification from electroencephalographic (EEG) signals. The four arithmetical mental tasks considered are calculation, counting, reminding along with relaxation. The database of four subjects was collected and used as training dataset. The features have been extracted from the delta, theta, alpha and beta of EEG bands in frequency domain. These features were analyzed and classified using the Linear Discriminant Analysis (LDA) classifier. Cluster Pattern indicates that these four mental tasks can be classified and recognized using EEG signals.

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