Alcoholism Identification by Processing the EEG Signals Using Oscillatory Modes Decomposition and Machine Learning
Khandelwal, Sarika Salankar, Nilima Mian Qaisar, Saeed Raut, Archana
Khandelwal, Sarika
Salankar, Nilima
Mian Qaisar, Saeed
Raut, Archana
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2025-01
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Excessive alcoholism has the potential to disturb the nervous system. A timely identification and prevention can cure it. This work proposes a novel approach to detect alcoholism by processing the electroencephalogram (EEG) signals. It is based on the signal processing and machine learning algorithm. We have used the oscillatory mode decompositions to decompose the EEG signals in modes. First six modes are considered for second order difference plots (SODPs). The central tendency measure, area, and mean are chosen as the three features from each intended SODP. Experiments are carried out by taking into account features collected from three different length time windows in order to establish an appropriate EEG signal segment length for the intended application. For classification, different machine learning algorithms are used.
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Advances in Neural Engineering Volume 2