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Khandare, Anand
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Applied Machine Learning in EEG data Classification to Classify Major Depressive Disorder by Critical Channels Dhekane, Sudhir; Khandare, Anand
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.719

Abstract

The electroencephalogram (EEG) stands out as a promising non-invasive tool for assessing depression. However, the efficient selection of channels is crucial for pinpointing key channels that can differentiate between different stages of depression within the vast dataset. This study outcome a comprehensive strategy for optimizing EEG channels to classify Major Depressive Disorder (MDD) using machine learning (ML) and deep learning (DL) approaches, and monitor effect of central lobe channels. A thorough review underscores the vital significance of EEG channel selection in the analysis of mental disorders. Neglecting this optimization step could result in heightened computational expenses, squandered resources, and potentially inaccurate classification results. Our assessment encompassed a range of techniques, such as Asymmetric Variance Ratio (AVR), Amplitude Asymmetry Ratio (AAR), Entropy-based selection employing Probability Mass Function (PMF), and Recursive Feature Elimination (RFE) where, RFE exhibited superior performance, particularly in pinpointing the most pertinent EEG channels while including central lobe channels like Fz, Cz, and Pz. With this accuracy between 97 to 99% is recorded by Electroencephalography Neural Network (EEGNet). Our experimental findings indicate that, models using RFE achieved enhancement in accuracy to classifying depressive disorders across diverse classifiers: EEGNet (96%), Random Forest (95%), Long Short-Term Memory (LSTM: 97.4%), 1D-CNN with 95%, and Multi-Layer Perceptron (98%) irrespective of central lobe incorporation. A pivotal contribution of this research is the development of a robust Multilayer Perceptron (MLP) model trained on EEG data from 382 participants, achieved accuracy of 98.7%, with a perfect precision score of 1.00, F1-Score of 0.983, and a Recall-Score of 0.966, to make it an enhanced technique for depression classification. Significant channels identified include Fp1, Fp2, F7, F4, F8, T3, C3, Cz, T4, T5, and P3, offering critical insights about depression. Our findings shows that, optimized EEG channel selection via RFE enhances depression classification accuracy in the field of brain-computer interface.
Optimized EEG-Based Depression Detection and Severity Staging Using GAN-Augmented Neuro-Fuzzy and Deep Learning Models Dhekane, Sudhir; Khandare, Anand
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 4 (2025): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i4.1107

Abstract

Detecting depression and identifying its severity remain challenging tasks, especially in diverse environments where fair and reliable outcomes are expected. This study aims to address this problem with advanced machine learning models to achieve high accuracy and explainability; making the approach suitable for the real world depression screening and stage evaluation by implementing EEG-based depression detection and staging. We established the parameters of development of EEG-based depression detection in optimization of channel selection together with machine-learning models. Extreme channel selection was performed during this study with Recursive Feature Elimination (RFE) whereby major 11 channels identified, and the MLP classifier achieved 98.7% accuracy supported by AI explainability, thus outpacing the XGBoost and LGBM by 5.2 to 8.2% across multiple datasets (n=184 to 382) and greatly endorsed incredible generalization (precision=1.000, recall=0.966). This makes MLP a trustworthy BCI tool for real-world implementation of depression screening. We also examined assigning depression stages (Mild/Moderate/Severe) on EEG data with models supported or not with GAN-based augmentation (198 to 5,000 samples). CNNs did well on Moderate-stage classification, while ANFIS kept a firm accuracy of 98.34% at perfect metric consistency (precision/recall=0.98) with AI explainability. GAN augmentation improved the classifications of severe cases by 15%, indicating a good marriage of neuro-fuzzy systems and synthetic data for the precise stage determination. This is an important contribution to BCI research since it offers a data-efficient and scalable framework for EEG based depression diagnosis and severity evaluation, thus contributing to the bridge between competitive modeling and clinical applicability. This work, therefore, lays down a pathway for the design of accessible and automated depression screening aids in both high-resource and low-resource settings
CIT-LieDetect: A Robust Deep Learning Framework for EEG-Based Deception Detection Using Concealed Information Test Nagale, Tanmayi; Khandare, Anand
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1300

Abstract

Deception detection with electroencephalography (EEG) is still an open problem as a result of inter-individual variability of brain activity and neural dynamics of deceitful responses. Traditional methods fail to perform well in terms of consistent generalization, and as a result, research has ahifted towards exploring sophisticated deep learning methods for Concealed Information Tests (CIT). The objective of the present study is to categorize subjects as guilty or innocent based on EEG measurements and rigorously test model performance in terms of accuracy, sensitivity, and specificity. To achieve this, experiments were conducted on two EEG datasets: the LieWaves dataset, consisting of 27 subjects recorded with five channels (AF3, T7, Pz, T8, AF4), and the CIT dataset, comprising 79 subjects recorded with 16 channels (Fp1, Fp2, F3, F4, C3, C4, Cz, P3, P4, Pz, O1, O2, T3/T7, T4/T8, T5/P7, T6/P8). Preprocessing involved a band-pass filter for noise reduction, followed by feature extraction using the Discrete Wavelet Transform (DWT) and the Fast Fourier Transform (FFT). Three models were evaluated: FBC-EEGNet, InceptionTime-light, and their ensemble. Results indicate that InceptionTime-light achieved the highest accuracy of 79.2% on the CIT dataset, surpassing FBC-EEGNet (70.8%). On the LieWaves dataset, FBC-EEGNet achieved superior performance, with 71.6% accuracy, compared with InceptionTime-light (65.93%). In terms of specificity, FBC-EEGNet reached 93.7% on the CIT dataset, while InceptionTime-light demonstrated balanced performance with 62.5% sensitivity and 87.5% specificity. Notably, the ensemble model provided stable and generalizable outcomes, yielding 70.8% accuracy, 62.5% sensitivity, and 75% specificity on the CIT dataset, confirming its robustness across subject groups. In conclusion, FBC-EEGNet is effective for maximizing specificity, InceptionTime-light achieves higher accuracy, and the ensemble model delivers a balanced trade-off. The implications of this work are to advance reliable EEG-based deception detection and to set the stage for future research on explainable and interpretable models, validated on larger and more diverse datasets.
Optimized Metaheuristic Integrated Neuro-Fuzzy Deep Learning Framework for EEG-Based Lie Detection Nagale, Tanmayi; Khandare, Anand
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1340

Abstract

EEG-based deception detection remains challenging due to three critical limitations: high inter-subject variability, which restricts generalization, the black-box nature of deep learning models that undermines forensic interpretability, and substantial computational overhead arising from high-dimensional multi-channel EEG data. Although recent state-of-the-art approaches report accuracies of 82–88%, they fail to provide the transparency required for legal and forensic admissibility. To address these limitations, this study aims to develop an accurate, computationally efficient, and explainable EEG-based deception detection framework suitable for real-world forensic applications. The primary contribution of this work is a novel hybrid neuro-fuzzy architecture that jointly integrates intelligent channel selection, complementary deep feature learning, and transparent fuzzy reasoning, enabling high performance without sacrificing interpretability. The proposed framework follows a five-stage pipeline: (1) intelligent channel selection using Type-2 fuzzy inference with ANFIS-based ranking and multi-objective evolutionary optimization (MOEA/D), reducing EEG dimensionality from 64 to 14 channels (78.1% reduction); (2) dual-path deep learning that combines EEGNet for spatial–temporal feature extraction with InceptionTime-Light for multi-scale temporal representations; (3) a fuzzy attention mechanism to generate interpretable feature importance weights; (4) an ANFIS-based classifier employing Takagi–Sugeno fuzzy rules for transparent decision-making; and (5) triple-level interpretability through channel importance visualization, attention-weighted features, and extractable linguistic rules. The framework is evaluated on two benchmark datasets, such as LieWaves (27 subjects, 5-channel EEG) and the Concealed Information Test (CIT) dataset (79 subjects, 16-channel EEG). Experimental results demonstrate superior performance, achieving 93.8% accuracy on LieWaves and 92.7% on the CIT dataset, representing an improvement of 5.3 % points over the previous best-performing methods, while maintaining balanced sensitivity (92.4%) and specificity (95.2%). In conclusion, this work establishes that neuro-fuzzy integration can simultaneously achieve high classification accuracy, computational efficiency, and forensic-grade explainability, thereby advancing the practical deployment of EEG-based deception detection systems in real-world forensic applications.
Co-Authors Dhekane, Sudhir Nagale, Tanmayi