Discrete to Continuous Algorithm for Optimal Channel Selection to Detect Alcoholism through EEG Recordings

Abstract

Alcoholism is a serious public health issue, and early diagnosis of this brain disease can be performed by analyzing Electroencephalogram (EEG) signals. However, the high dimensionality of EEG datasets requires significant computational time and resources for the automatic processing of EEG signals. This study proposes a novel method to reduce EEG dataset dimensionality using a Discrete to Continuous algorithm (DtC) by selecting optimal EEG channels. The DtC approach compares alcoholic and nonalcoholic EEG signals as two-time series in a two-dimensional space based on a distance measurement between the two-time series. The Dynamic Time Warping (DTW) algorithm is used to compare the performance of the DtC approach. Classification performance metrics were evaluated for both the DtC and DTW algorithms. The optimal selected channels by our approach are the C3, CP5, PO7, and F8 channels with accuracy values of 100, 100, 94 and 81%, respectively. These findings are consistent with previous research on statistical analysis and machine learning methods and with the DTW algorithm results. Our findings are also in line with scientific evidence from clinical research. The DtC approach was efficient in selecting the best channels to reduce the EEG dataset dimensionality, allowing us to select four out of the 64 EEG channels (C3, CP5, PO7, and F8) that retain essential information related to alcoholism, which is useful in reducing computational time and resources during the classification task of alcoholic EEG.