AI-Based Neuromorphic Co-Processor for Cognitive Rehabilitation

Sub-Project 6

OVERVIEW

This sub-project explores next-generation, scalable, AI-enabled neuromorphic platforms for cognitive rehabilitation and neurofeedback applications. The vision is to investigate compact, adaptive, and scalable Brain-Computer Interface (BCI) technologies that support real-time interaction between neural activity and intelligent feedback systems. The work focuses on enabling future portable neurotechnology platforms that are energy-efficient, responsive, and suitable for real-world deployment.

MOTIVATION

Current neurofeedback and cognitive rehabilitation systems face several important challenges, including limited portability, processing latency, scalability constraints, and high-power consumption. As cognitive neurotechnology evolves toward more continuous and accessible use, there is growing interest in systems that can operate efficiently outside traditional laboratory settings while still supporting real-time adaptive processing.

This sub-project investigates how brain-inspired computing and low-power AI architectures can contribute toward future cognitive rehabilitation systems that are more scalable, accessible, and practical for everyday environments.

RESEARCH DIRECTION

The sub-project explores multiple interconnected research themes, including:

Real-time AI-assisted neural signal processing

Closed-loop neurofeedback systems

Neuromorphic and event-driven computing approaches

Low-power edge intelligence for neural interfaces

Scalable architectures for future high-density Brain-Computer Interfaces

A key area of interest is the use of sparsity-driven and neuromorphic frontends inspired by the efficiency of biological neural systems. These approaches have the potential to reduce computational overhead and enable compact, responsive systems suitable for wearable or portable neurotechnology platforms.

The research also investigates modular and scalable architectures that may support future generations of high-density neural interface systems while maintaining efficiency and adaptability.

DEVELOPMENT STAGES

This sub-project goes through several stages:

Development of compact AI-enabled neurofeedback platforms focused on real-time signal processing and portable edge computing.
Exploration of neuromorphic co-processing architectures designed for low-power, scalable, and adaptive neural decoding.
Investigation of modular high-density architectures intended to support future large-scale neural interface ecosystems.

IMPACT

This research aims to advance long-term intelligent cognitive rehabilitation technologies by exploring new paradigms in AI-enabled neural processing and portable neurofeedback systems.

Potential future applications include: