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

arXiv: https://arxiv.org/abs/2505.02516 · HTML: https://mlsanity.com/paper/2505.02516/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 5 May 2025.

## Builds on (most cited first, 11 of 11)

- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [EEG-Based Emotion Recognition Using Regularized Graph Neural Networks](https://mlsanity.com/paper/1907.07835/) (2019)
- [ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification](https://mlsanity.com/paper/2006.07900/) (2020)
- [REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates](https://mlsanity.com/paper/2406.16906/) (2024)
- [A 128 channel Extreme Learning Machine based Neural Decoder for Brain Machine Interfaces](https://mlsanity.com/paper/1509.07450/) (2015)
- [NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC](https://mlsanity.com/paper/2205.06090/) (2022)
- [Closed-Loop Neural Prostheses with On-Chip Intelligence: A Review and A Low-Latency Machine Learning Model for Brain State Detection](https://mlsanity.com/paper/2109.05848/) (2021)
- [Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs](https://mlsanity.com/paper/2204.02362/) (2022)
- [Intelligent and Miniaturized Neural Interfaces: An Emerging Era in Neurotechnology](https://mlsanity.com/paper/2405.10780/) (2024)
- [A 16-Channel Low-Power Neural Connectivity Extraction and Phase-Locked Deep Brain Stimulation SoC](https://mlsanity.com/paper/2207.01060/) (2022)
- [An Accurate and Hardware-Efficient Dual Spike Detector for Implantable Neural Interfaces](https://mlsanity.com/paper/2208.13432/) (2022)

## Abstract (arXiv)

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.