<![CDATA[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.]]>
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