Machine-Learning-Powered Neural Interfaces for Smart Prosthetics and Diagnostics

TL;DR

This review surveys the convergence of high-density neural interfaces with ML and energy-efficient SoCs to enable real-time neural decoding, neuromodulation, and prosthetic control. It contrasts traditional ML and deep learning approaches, highlighting on-chip feature extraction, interpretability, and hardware-aware efficiency. Concrete realizations include NeuralTree and SciCNN for diagnostics, and MiBMI with a DNC-based decoder for handwriting on a compact BCI, illustrating substantial gains in area and power efficiency. The paper argues for continued hardware-software co-design, privacy, and scalability to bring fully integrated, autonomous neural interfaces to clinical and daily-use settings.

Abstract

Advanced neural interfaces are transforming applications ranging from neuroscience research to diagnostic tools (for mental state recognition, tremor and seizure detection) as well as prosthetic devices (for motor and communication recovery). By integrating complex functions into miniaturized neural devices, these systems unlock significant opportunities for personalized assistive technologies and adaptive therapeutic interventions. Leveraging high-density neural recordings, on-site signal processing, and machine learning (ML), these interfaces extract critical features, identify disease neuro-markers, and enable accurate, low-latency neural decoding. This integration facilitates real-time interpretation of neural signals, adaptive modulation of brain activity, and efficient control of assistive devices. Moreover, the synergy between neural interfaces and ML has paved the way for self-sufficient, ubiquitous platforms capable of operating in diverse environments with minimal hardware costs and external dependencies. In this work, we review recent advancements in AI-driven decoding algorithms and energy-efficient System-on-Chip (SoC) platforms for next-generation miniaturized neural devices. These innovations highlight the potential for developing intelligent neural interfaces, addressing critical challenges in scalability, reliability, interpretability, and user adaptability.

Figures (3)

• Figure 1: Neural Interfaces: (a) Neural recording interfaces capture high-density neural signals and process them to reduce data rates, or wirelessly transmit them to an external computer for further processing. (b) Therapeutic neural interfaces extract neuro-markers to detect disease-related neurological symptoms or mental states, and may also integrate neurostimulation for functions such as seizure suppression or brain rewiring. (c) Prosthetic neural interfaces use ML to convert brain intention into actionable commands, enabling control of end-effectors like robotic hands.
• Figure 2: Efficient ML Models for Neural Decoding: (a) Residual State Updates (REST) for seizure detection Afzal2024REST. (b) Light Gradient-Boosting Machine (LightGBM) and SHapley Additive exPlanations (SHAP) for decoding anxiety-related behaviors liu2024neuralzhu2021closed. (c) Distinctive Neural Code (DNC) and Linear Discriminant Analysis (LDA) for brain-to-text decoding Shaeri2024MiBMI.
• Figure 3: Neural SoCs: (a) Die photo of the closed-loop NeuralTree chip and its experimental results in tremor and seizure detection shin2022neuraltree. (b) Die photo of the miniaturized brain-machine interface (MiBMI) chipset and its decoding results in the handwriting task YS2024JSSC.