Digital processing of electroencephalography (EEG) signals has now been popularly used
in a wide variety of applications such as seizure detection/prediction, motor imagery …

The screening test for alcohol use disorder (AUD) patients has been of subjective nature
and could be misleading in particular cases such as a misreporting the actual quantity of …

Following the research question and the relevant dataset, feature extraction is the most
important component of machine learning and data science pipelines. The wavelet …

When assessing human emotion using EEG classification, one of the critical problems is to
deal with the very large number of features to be classified. In this paper, we address this …

Screening alcohol use disorder (AUD) patients has been challenging due to the subjectivity
involved in the process. Hence, robust and objective methods are needed to automate the …

Background The abnormal alcohol consumption could cause toxicity and could alter the
human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately …

Alcoholism can be analyzed by Electroencephalogram (EEG) data. Finding an optimal
subset of EEG channels for alcoholism detection is a challenging task. The paper reports a …

Alcoholism is a socio-economical syndrome in which human being may lose his/her health
and wealth. The paper reports a novel approach for the rapid detection of alcoholism using …

Biometric-based cryptographic key generation is regarded as a data mining approach that
uses knowledge discovery techniques to extract biometric information to generate …

The problem of analyzing and identifying regions of high discrimination between alcoholics
and controls in a multichannel electroencephalogram (EEG) signal is modeled as a feature …