Thought-Driven AI: A Neuro-Intent Computing Framework for Direct Human–AI Interaction Through Neural Signal Interpretation and Autonomous Task Orchestration
Year: 2026
Type: preprint
Abstract: Human–computer interaction has traditionally relied on explicit communication mechanisms, including keyboards, graphical interfaces, touchscreens, and spoken language. Although these mechanisms have substantially improved the accessibility and usability of digital systems, they still require users to translate their intentions into externally observable commands. Brain–computer interfaces (BCIs) offer an alternative by establishing communication pathways between neural activity and computational systems. Recent developments in neural speech decoding, intracortical recording, and non-invasive electroencephalography (EEG) have demonstrated the potential to translate aspects of neural activity into communication outputs. However, conventional BCI architectures primarily emphasize neural signal acquisition, feature extraction, and classification. This paper proposes Thought-Driven AI (TDAI), a conceptual framework that extends neural signal interpretation toward intention-aware artificial intelligence interaction. Rather than treating neural activity as a direct command, TDAI models the process as a sequence of neural observation, personalized representation learning, intent hypothesis generation, uncertainty estimation, semantic intent reconstruction, authorization-aware task planning, and AI execution. The framework introduces six principal research mechanisms: (1) personalized neural intent representation, (2) hierarchical thought-to-intent transformation, (3) uncertainty-aware intent verification, (4) adaptive neural interaction, (5) intent-driven AI capability orchestration, and (6) feedback-based neural–AI co-adaptation. An important design principle is that neural signals should not automatically be interpreted as executable instructions. Instead, the system should distinguish between cognitive activity, attempted communication, intended action, and explicitly authorized execution. The potential advantages of TDAI include reduced dependence on conventional input modalities, more direct interaction with intelligent systems, alternative communication pathways for individuals with severe motor or speech impairments, personalized interaction, and the possibility of integrating neural intention with autonomous AI task execution. The framework also identifies technical and ethical limitations involving neural signal ambiguity, cross-session variability, privacy, unintended inference, and authorization. This paper establishes a conceptual and testable research direction for integrating brain–computer interfaces with modern artificial intelligence systems. It does not claim that unrestricted human thoughts can currently be decoded or that general-purpose thought-to-action interaction has been experimentally demonstrated.
Author: Zhang, Jincheng
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225597164