The aim of this work is to introduce a computer science solution to manage emotions and affections and connect them to the causes as in humans. The scientific foundation of this work lies in the ability to model the affective and emotional states of an individual or artificial intelligence (AI). Then, in this study, we go a step further by exploring how to extend this capability by linking it to the underlying causes—specifically, by establishing a connection between emotions, affective states, and neurotransmitter activities. The methods used in this study pertain to decision support systems based on complexity theory. Specifically, for the training of the platform to study the link between emotions/affections and neurotransmitters, an electroencephalogram (EEG) acquisition module is integrated into the platform. As a result, this solution provides the bedrock for next-generation AI, i.e., artificial rational–emotive decision-makers. In addition, this research studies the connection of EEG data with neurotransmitters’ activity, opening pathways to applications such as emotional monitoring, mental health, and brain–computer interfaces, adding to cognitively and emotionally enriched AI. The main result of this study is a platform able to manage artificial neurotransmitters such as adrenaline, GABA, dopamine, serotonin, oxytocin, endorphins, and the hormone cortisol for emulating and motivating emotive and affective states. In conclusion, this study highlights the following: (i) the possibility of conducting indirect measurements of emotional states based on EEG data, (ii) the development of a framework capable of generating a wide spectrum of emotional states by modulating neurotransmitter levels within a defined discrete range, and (iii) the ability to establish a connection between neurotransmitters (causes) and emotional states (effects).

The digital transformation in the treatment of mental health and emotional disharmony requires artificial intelligence architectures that overcome the limitations of purely neural approaches, such as temporal inconsistency, opacity, and lack of theoretical foundations. Assuming the existence and use of generalist LLMs currently used in clinical settings and considering the appropriate limitations indicated by experts, this article aims to offer clinicians an alternative Neuro-symbolic-Psychological multi-agent architecture (NSPA-AI), which integrates archetypal symbolic reasoning with neurobiological modelling, based on our established framework of artificial neurotransmitters for the modelling and analysis of affective-emotional stimuli to enable interpretable AI-assisted psychological intervention. The system implements a hub-and-spoke topology that coordinates five specialized agents (symbolic, psychological, neurofunctional, decision fusion, learning) that process heterogeneous information via SPADE protocols. Seven archetypal constructs from Jungian psychology and narrative identity theory provide stable symbolic frameworks for longitudinal therapeutic consistency. An empirical study of 156 university students demonstrated significant improvements in depression (Cohen’s d = 1.03), stress (d = 0.89), and narrative identity integration (d = 0.75), which were maintained at a 12-week follow-up and superior to GPT-4 controls (d = 0.34). Neurofunctional correlations—downregulation of cortisol (r = 0.71 with stress reduction), increase in serotonin (r = −0.68 with depression improvement)—validated the neurobiological basis of the entropy-energy framework. Qualitative analysis revealed the following four mechanisms of improvement: symbolic emotional support (93%), increased self-awareness through neurotransmitter visualization (84%), non-judgmental AI interaction (98%), and archetypal narrative organization (87%). The results establish that neuro-symbolic architectures are viable alternatives to large language models for digital mental health, providing the interpretability and clinical validity essential for adoption in the healthcare sector.

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.