# Deception Detection from Five-Channel Wearable EEG on LieWaves: A Reproducible Baseline for Subject-Dependent and Subject-Independent Evaluation

## TL;DR

This paper evaluates deception detection using five-channel wearable EEG data, showing modest cross-subject accuracy but highlighting the need for better generalization and standardized protocols.

## Contribution

A reproducible benchmark for deception detection using five-channel EEG with explicit protocols for subject-dependent and subject-independent evaluations.

## Key findings

- A subject-independent model achieved 66.70% session-level accuracy with an AUC of 57.80%.
- A subject-dependent model reached 99.94% window-level accuracy, but this is an optimistic upper bound.
- Results suggest limited but above-chance lie-truth discrimination with five-channel EEG in controlled settings.

## Abstract

What are the main findings?
We evaluate deception detection from five-channel wearable EEG on the public LieWaves dataset (27 subjects, Emotiv Insight headset) under both subject-dependent (same-subject) and subject-independent (cross-subject) protocols.In the subject-independent configuration, a compact ResNet-SE model trained on raw overlapping windows and evaluated with a single session-level decision threshold estimated from cross-validated scores attains 66.70% session-level accuracy (AUC = 57.80%) on subjects not seen by the model that predicts them.In the subject-dependent configuration, an overlapping short-window Res-TCN-SE-Attention model that fuses raw EEG with DWT-based spectral and handcrafted band-power and Hjorth features reaches 99.94% window-level accuracy on held-out windows from held-out sessions of the same individuals under a heavily overlapping, subject-dependent but session-disjoint split. This near-ceiling window-level figure should be interpreted only as an optimistic upper bound in an autocorrelated, subject-dependent regime, and not as an estimate of deployment-relevant deception-detection ability.

We evaluate deception detection from five-channel wearable EEG on the public LieWaves dataset (27 subjects, Emotiv Insight headset) under both subject-dependent (same-subject) and subject-independent (cross-subject) protocols.

In the subject-independent configuration, a compact ResNet-SE model trained on raw overlapping windows and evaluated with a single session-level decision threshold estimated from cross-validated scores attains 66.70% session-level accuracy (AUC = 57.80%) on subjects not seen by the model that predicts them.

In the subject-dependent configuration, an overlapping short-window Res-TCN-SE-Attention model that fuses raw EEG with DWT-based spectral and handcrafted band-power and Hjorth features reaches 99.94% window-level accuracy on held-out windows from held-out sessions of the same individuals under a heavily overlapping, subject-dependent but session-disjoint split. This near-ceiling window-level figure should be interpreted only as an optimistic upper bound in an autocorrelated, subject-dependent regime, and not as an estimate of deployment-relevant deception-detection ability.

What are the implications of the main findings?
Five-channel wearable EEG thus carries modest but above-chance information for lie–truth discrimination in cross-subject settings, suggesting that portable screening might be technically feasible only in tightly controlled scenarios. At the same time, the pronounced generalization gap between subject-dependent and subject-independent models underscores that substantial additional work on robustness, calibration, and external validation is required before real-world deployment.By making our subject-wise splits, windowing rules, decision thresholds, and session-level aggregation explicit, we provide a concrete, reproducible benchmark for five-channel wearable EEG deception detection and highlight the need for standardized, clearly reported evaluation protocols.

Five-channel wearable EEG thus carries modest but above-chance information for lie–truth discrimination in cross-subject settings, suggesting that portable screening might be technically feasible only in tightly controlled scenarios. At the same time, the pronounced generalization gap between subject-dependent and subject-independent models underscores that substantial additional work on robustness, calibration, and external validation is required before real-world deployment.

By making our subject-wise splits, windowing rules, decision thresholds, and session-level aggregation explicit, we provide a concrete, reproducible benchmark for five-channel wearable EEG deception detection and highlight the need for standardized, clearly reported evaluation protocols.

Deception detection with low-channel wearable EEG requires protocols that generalize across people while remaining practical for portable devices. Using the public LieWaves dataset (27 subjects recorded with a five-channel Emotiv Insight headset), we evaluate to what extent five-channel head-mounted EEG can support lie–truth discrimination under both subject-independent and subject-dependent evaluations. For the subject-independent setting, we train a compact Residual Network with Squeeze-and-Excitation blocks (ResNet-SE) model on raw overlapping windows with focal loss, light data augmentation, and grouped cross-validation by subject; out-of-fold window probabilities are averaged per session and converted to labels using a single decision threshold estimated from the cross-validated session scores. For the subject-dependent setting, we adopt an overlapping short-window Residual Temporal Convolutional Network with Squeeze-and-Excitation and Attention (Res-TCN-SE-Attention) model that fuses raw EEG with discrete wavelet transform (DWT)-based spectral and handcrafted band-power and Hjorth features, using an 80/10/10 split at the recording/session level (stratified by session label), so that all windows from a given session are assigned to a single subset; because each subject contributes two sessions, the same subject may still appear across subsets via different sessions. The subject-independent model attains 66.70% session-level accuracy with an AUC of 0.58 on unseen subjects, underscoring the difficulty of person-independent generalization from low-channel wearable EEG. Because practical deployment requires generalization to previously unseen individuals, we treat the subject-independent evaluation as the primary estimate of real-world generalization. In contrast, the subject-dependent pipeline reaches 99.94% window-level accuracy under the overlapping sliding-window (OSW) setting with a session-disjoint split (no session contributes windows to more than one subset). This near-ceiling performance reflects the optimistic nature of subject-dependent evaluation with highly overlapping windows, even when avoiding within-session train–test overlap, and should not be interpreted as a meaningful indicator of deception-detection capability under realistic deployment constraints. These results suggest limited, above-chance separability between lie and truth sessions in LieWaves using a five-channel wearable EEG under the studied protocol; however, performance remains far from deployment-ready and is strongly shaped by evaluation design. Explicit reporting of both protocols, together with clear rules for windowing, aggregation, and threshold selection, supports more reproducible and comparable benchmarking.