Alcoholism classification based on EEG data using Independent Component Analysis (ICA), Wavelet de-noising and Probabilistic Neural Network (PNN)

Rachman, Nurindah Tiffani; Tjandrasa, Handayani; Fatichah, Chastine

doi:10.1109/isitia.2016.7828626

Cited by 13 publications

(10 citation statements)

References 8 publications

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“…To the best of the authors' knowledge, this is the first attempt of discriminating the mental state related to different alcohol doses. Owning to the different, consecutive alcohol doses of our method, the proposed approach has more potentials than a simple discrimination between drunk and no-drunk people as have previously presented in [12][13][14][15]. Our approach introduces a 4-class classification problem, wherein each class represents the different mental state as depicted from EEG recordings obtained while participants consumed different alcohol doses.…”

Section: Discussionmentioning

confidence: 99%

“…During the last decades, brain waves related to alcohol consumption have gained the research interest. Most of the studies [12][13][14][15] focused on alcoholic patients and the subtle EEG changes, which can differentiate normal individuals from alcoholic patients. Other groups of researchers employed normal subjects which consumed alcohol either performing a task [16][17][18][19][20] or staying passive in resting state [21].…”

Section: Related Workmentioning

confidence: 99%

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Direct Assessment of Alcohol Consumption in Mental State Using Brain Computer Interfaces and Grammatical Evolution

et al. 2018

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Alcohol consumption affects the function of the brain and long-term excessive alcohol intake can lead to severe brain disorders. Wearable electroencephalogram (EEG) recording devices combined with Brain Computer Interface (BCI) software may serve as a tool for alcohol-related brain wave assessment. In this paper, a method for mental state assessment from alcohol-related EEG recordings is proposed. EEG recordings are acquired with the Emotiv EPOC+, after consumption of three separate doses of alcohol. Data from the four stages (alcohol-free and three levels of doses) are processed using the OpenViBE platform. Spectral and statistical features are calculated, and Grammatical Evolution is employed for discrimination across four classes. Obtained results in terms of accuracy reached high levels (89.95%), which renders the proposed approach suitable for direct assessment of the driver’s mental state for road safety and accident avoidance in a potential in-vehicle smart system.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Direct Assessment of Alcohol Consumption in Mental State Using Brain Computer Interfaces and Grammatical Evolution

et al. 2018

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“…14 Using these five wavelet families at level 4 and level 6 decomposition, 12 statistical features were extracted from the input mammogram images. According to the literature, 15,16 the decomposition levels of 4 and 6 are less susceptible to noise, and the features are highly predominant at this level of decomposition. Hence, decomposition levels 4 and 6 are chosen to obtain better classification accuracy.…”

Section: Selection Of Wavelet Families and Targetmentioning

confidence: 99%

“…According to the literature, 15 For the analysis, 80 mammogram images (40 benign and 40 malignant cancer affected images) were used. Wavelet selection depends on the properties of the wavelets.…”

Section: Selection Of Wavelet Families and Targetmentioning

confidence: 99%

Detection and classification of microcalcification from digital mammograms with firefly algorithm, extreme learning machine and non‐linear regression models: A comparison

Chakravarthy

Rajaguru

2019

Int J Imaging Syst Tech

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In this study, abnormalities in medical images are analysed using three classifiers, and the results are compared. Breast cancer remains a major public health problem among women worldwide. Recently, many algorithms have evolved for the investigation of breast cancer diagnosis through medical imaging. A computer‐aided microcalcification detection method is proposed to categorise the nature of breast cancer as either benign or malignant from input mammogram images. The standard mammogram image corpus, the Mammogram Image Analysis Society database is utilised, and feature extraction is performed using five different wavelet families at level 4 and level 6 decomposition. The work is accomplished through firefly algorithm (FA), extreme learning machine (ELM) and least‐square‐based non‐linear regression (LSNLR) classifiers. The performance of the classifiers is compared by benchmark metrics, such as total error rate, specificity, sensitivity, area under the receiver operating characteristic curve, precision, F1 score and the Matthews correlation coefficient. As validation of the classifier results, a kappa analysis is included to determine the agreement among classifiers. The LSNLR classifier attains a 3% to 7% improvement in average accuracy compared with the average classification accuracy of the FA (86.75%) and ELM (90.836%) classifiers.

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“…Using the electrical impulses that represent the physiological functions like eye blinking and heart beating, Rachman et al ( 2016 ) proposed an independent component analysis through EEG signals. In their work, the features extracted by stationary wavelet transform with Daubechies decomposition at level 6 were combined with a probabilistic neural network to classify samples from 64 channels into two classes: healthy and alcoholism patients.…”

Section: Overview Of the Alcoholism Predisposition Classificationmentioning

confidence: 99%

Bi-Dimensional Approach Based on Transfer Learning for Alcoholism Pre-disposition Classification via EEG Signals

Zhang

Silva

Ohata

et al. 2020

Front. Hum. Neurosci.

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Alcoholism classification based on EEG data using Independent Component Analysis (ICA), Wavelet de-noising and Probabilistic Neural Network (PNN)

Cited by 13 publications

References 8 publications

Direct Assessment of Alcohol Consumption in Mental State Using Brain Computer Interfaces and Grammatical Evolution

Direct Assessment of Alcohol Consumption in Mental State Using Brain Computer Interfaces and Grammatical Evolution

Detection and classification of microcalcification from digital mammograms with firefly algorithm, extreme learning machine and non‐linear regression models: A comparison

Bi-Dimensional Approach Based on Transfer Learning for Alcoholism Pre-disposition Classification via EEG Signals

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