NEUROFUSION: Convergence of Artificial Intelligence and Brain-Computer Interfaces — Architectural Taxonomies, Clinical Applications, Implementation Bottlenecks, and Ethical Frontiers
Year: 2026
Type: preprint
Abstract: The bi-directional integration of artificial intelligence (AI) with biological neuroscience—herein conceptualized under the paradigm of Neurofusion—represents one of the most transformative cybernetic frontiers in contemporary science. The mammalian central nervous system constitutes an ultra-dense, self-organizing dynamical computational network operating across multiple spatiotemporal scales. However, translating complex, non-stationary, and low signal-to-noise electrophysiological telemetry into actionable computational directives remains constrained by biological, algorithmic, and engineering bottlenecks. This paper presents a comprehensive analytical treatise on the convergence of modern machine learning paradigms with invasive and non-invasive brain-computer interfaces (BCIs). We establish a systematic architectural taxonomy categorizing acquisition modalities (electroencephalography, electrocorticography, and intracortical microelectrode arrays) and evaluate contemporary deep neural decoders, spanning spatial-temporal convolutional networks, state-space representations, and self-attention transformers. Furthermore, we examine major clinical and augmentative applications: closed-loop motor neuroprosthetics, continuous seizure prediction and adaptive deep brain stimulation (DBS), cognitive restoration via synthetic hippocampal memory prostheses, and neuro-adaptive immersive virtual environments. Critically, we dissect acute physical and biological implementation bottlenecks, including glial scarring, chronic electrode micromotion, thermal dissipation limits (<1°C cortical heating), and telemetry bandwidth. To mitigate these constraints, we propose an architectural framework integrating neuromorphic edge-computing, event-driven spiking neural networks (SNNs), and homomorphic neural telemetry encryption. Finally, we formulate a formal governance matrix operationalizing the five foundational Neurorights principles (mental privacy, cognitive liberty, personal identity, fair access, and algorithmic bias protection), providing an actionable roadmap for ethically verified, clinically viable neurofusion systems.
Authors: SINGH, MAYANK, BHARDWAJ, MAYANK
Institution: Guru Gobind Singh Indraprastha University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7223697569