ANALYSIS OF EEG SIGNALS IN SPATIAL, SPECTRAL AND TEMPORAL DOMAINS FOR CLASSIFICATION

Abstract

EEG signals serve as a non-invasive, real-time biomarker of brain function, offering sensitive metrics for diagnosing and monitoring various neurological and psychiatric disorders. Their ability to capture subtle changes in electrical brain activity makes EEG an invaluable tool for detecting patterns and dysfunctions underlying conditions like Alcohol Use Disorder (AUD) and Attention-Deficit/Hyperactivity Disorder (ADHD). Unlike subjective behavioral assessments, EEG provides objective, quantifiable metrics that reflect the dynamic interplay of neural networks across temporal, spectral, and spatial domains. This thesis introduces a comprehensive set of twenty seven Quantitative EEG (QEEG) features to create a detailed and multifaceted representation of brain activity. These neuro- biomarkers are grouped into three main categories. Power features quantify the signal's strength and statistical properties, including total amplitude power, standard deviation, skewness, kurtosis, and both the mean and standard deviation of the signal envelope, reflecting the strength, variability, and asymmetry of neural oscillations. In a similar category, Range EEG features (rEEG) further probe peak-to-peak dynamics with statistics like mean, median, lower and upper percentile margins, width, coefficient of variation, asymmetry, and standard deviation, offering a richly detailed view of voltage fluctuations across windows. Spectral features analyze the frequency components and complexity by measuring spectral absolute power and relative power, Shannon entropy, spectral flatness, spectral difference, spectral edge frequency, permutation entropy and fractal dimension, revealing abnormalities in brain rhythms. The third category is Inter-Hemispherical Connectivity features that measure the interaction between the brain's left and right hemispheres using metrics like the Brain Symmetry Index (BSI), correlation, mean and maximum coherence, and the frequency of maximum coherence, which are crucial for understanding network-level dysfunction. Another significant contribution is the development of a robust, generalized end-to-end signal processing and feature selection pipeline that converts raw EEG recordings into QEEG biomarkers for accurate diagnosis of behavioral and neurological disorders. The process begins with artifact removal and referencing to prepare high-quality, clean EEG data. This is followed by sub-time segmentation using overlapping temporal windows to preserve the continuity of neural dynamics over time. The resulting EEG signal is subjected to a vii broad-band spectral filter bank, dividing the signal into ten non-overlapping frequency bands covering the full range from 0–100Hz, ensuring that both slower and faster oscillations (including high-gamma activity) are analyzed. Within each spectral band, common spatial pattern (CSP) filtering pinpoints the most class-informative spatial components, maximizing discriminability between healthy and patient subject groups and reducing the influence of irrelevant channels. From these spectrally and spatially filtered signals, the twenty seven QEEG features are extracted, and subsequently averaged over different temporal windows, producing a high-dimensional feature space that enables comprehensive modelling of neural function. To address redundancy and highlight only the most predictive features, advanced filter-wrapper feature selection is employed to identify the most discriminant features. An ensemble feature selection approach is used: initially, filter-based methods such as ANOVA, Chi-square, Gini Index, and Information Gain Ratio statistically rank the features, the obtained ranks are averaged, and a wrapper technique—typically Sequential Forward Selection (SFS)—iteratively builds the optimal feature subset by maximizing classifier performance with cross-validation. This process reduces the feature set into a compact, highly informative set, supporting models that generalizes accurately across independent patient cohorts and multiple brain disorders. Finally, a novel and generalized framework is designed to extract Functional Connectivity features by capturing linear monotonic inter-channel associations, enabling robust identification of functional interactions between distinct brain regions. Functional connectivity in the time domain is quantified using the Pearson correlation coefficient, serving as a quantitative EEG (QEEG) feature that captures inter-electrode synchrony and underlying neural patterns to enhance the accuracy of disorder classification. The result is a fully integrated and computationally efficient pipeline that combines broad-spectrum feature capture, performs network-level analysis, and rigorous feature reduction to reach state-of- the-art accuracy in classifying behavioral and neurological disorders successfully.