<![CDATA[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.]]>
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