Wearable-Oriented Neurotransmitter-Inspired EEG Bioelectronics: An Interpretable Feature Taxonomy for Affective Classification and Exploratory Sleep-Onset Transfer Analysis

Wearable and intelligent bioelectronic systems are emerging as a key enabling technology for continuous, non-invasive health monitoring, coupling physiological sensing with data-driven inference. Within this paradigm, electroencephalography (EEG) provides a wearable-compatible biosensing modality for capturing the pre-sleep neurophysiological dynamics linked to emotional regulation and sleep onset. Insomnia affects approximately 10–15% of adults worldwide and is often associated with dysregulated emotions and pre-sleep hyperarousal. Existing EEG-based affective and sleep-onset processing pipelines often rely either on deep-learning architectures with limited interpretability or on hand-crafted spectral descriptors with weak theoretical motivation. This study presents an exploratory proof-of-principle bioelectronic processing framework in which EEG sensing features are organized according to ANT-7 (artificial neurotransmitter seven-dimensional model), a neurotransmitter-inspired computational taxonomy introduced as a heuristic feature-design prior rather than as a validated neurochemical theory. The proposed feature set includes the alpha/theta power ratio, sample entropy, Higuchi fractal dimension, and phase-locking value extracted from the public DREAMER and DEAP datasets (23 and 32 subjects, respectively). SVM, Random Forest, and 1D-CNN classifiers are trained under subject-independent leave-one-subject-out cross-validation with strict within-fold normalization to prevent data leakage, and interpretability is assessed through SHAP values and permutation importance (PI). To stress-test whether this feature organization transfers beyond the affective benchmarks on which it is trained, classifier outputs are then related to sleep-onset latency in Sleep-EDF Expanded through a deliberately cautious cross-dataset transfer analysis. Within this protocol, the best model reaches 88.4% accuracy in three-class affective-state recognition (stress/neutral/relaxed; AUC-ROC = 0.93). As an exploratory secondary analysis, classifier-derived relaxation estimates show a statistically significant negative association with polysomnographic sleep-onset latency and improve over a single alpha/theta-ratio baseline; this cross-dataset result is reported as a proof of concept, not as a validated sleep-onset predictor. Interpretability analyses (SHAP and permutation importance) indicate that the learned feature rankings are internally consistent with the neurotransmitter-inspired feature design, a property we interpret as internal coherence rather than as independent confirmation of the taxonomy. Together, these elements outline a complete sensor-to-AI processing chain—from EEG biosensing, through neurotransmitter-inspired signal-feature extraction, to interpretable and computationally lightweight inference—designed for compatibility with low-density wearable EEG devices and edge deployment. However, EEG does not measure neurotransmitter concentrations, the study does not benchmark ANT-7 directly against competing taxonomies such as valence-arousal/circumplex or RDoC-inspired feature organizations, and the Sleep-EDF analysis should not be interpreted as evidence that the model measures a validated latent construct of sleep readiness. Accordingly, the manuscript should be read as a framework-validation study of one interpretable feature taxonomy, not as a theory-validation study of ANT-7 or as a clinical validation study.