Credit: Alain Herzog
EPFL researchers have combined low-power chip design, machine learning algorithms, and soft implantable electrodes to produce a neural interface that can identify and suppress symptoms of various neurological disorders.
Mahsa Shoaran of the Integrated Neurotechnologies Laboratory in the School of Engineering collaborated with Stéphanie Lacour in the Laboratory for Soft Bioelectronic Interfaces to develop NeuralTree: a closed-loop neuromodulation system-on-chip that can detect and alleviate disease symptoms. Thanks to a 256-channel high-resolution sensing array and an energy-efficient machine learning processor, the system can extract and classify a broad set of biomarkers from real patient data and animal models of disease in-vivo, leading to a high degree of accuracy in symptom prediction.
“NeuralTree benefits from the accuracy of a neural network and the hardware efficiency of a decision tree algorithm,” Shoaran says. “It’s the first time we’ve been able to integrate such a complex, yet energy-efficient neural interface for binary classification tasks, such as seizure or tremor detection, as well as multi-class tasks such as finger movement classification for neuroprosthetic applications.”
Their results were presented at the 2022 IEEE International Solid-State Circuits Conference and published in the IEEE Journal of Solid-State Circuits, the flagship journal of the integrated circuits community.
Efficiency, scalability, and versatility
NeuralTree functions by extracting neural biomarkers – patterns of electrical signals known to be associated with certain neurological disorders – from brain waves. It then classifies the signals and indicates whether they herald an impending epileptic seizure or Parkinsonian tremor, for example. If a symptom is detected, a neurostimulator – also located on the chip – is activated, sending an electrical pulse to block it.
Shoaran explains that NeuralTree’s unique design gives the system an unprecedented degree of efficiency and versatility compared to the state-of-the-art. The chip boasts 256 input channels, compared to 32 for previous machine-learning-embedded devices, allowing more high-resolution data to be processed on the implant. The chip’s area-efficient design means that it is also extremely small (3.48mm2), giving it great potential for scalability to more channels. The integration of an ‘energy-aware’ learning algorithm – which penalizes features that consume a lot of power – also makes NeuralTree highly energy efficient.
In addition to these advantages, the system can detect a broader range of symptoms than other devices, which until now have focused primarily on epileptic seizure detection. The chip’s machine learning algorithm was trained on datasets from both epilepsy and Parkinson’s disease patients, and accurately classified pre-recorded neural signals from both categories.
“To the best of our knowledge, this is the first demonstration of Parkinsonian tremor detection with an on-chip classifier,” Shoaran says.
Self-updating algorithms
Shoaran is passionate about making neural interfaces more intelligent to enable more effective disease control, and she is already looking ahead to further innovations.
“Eventually, we can use neural interfaces for many different disorders, and we need algorithmic ideas and advances in chip design to make this happen. This work is very interdisciplinary, and so it also requires collaborating with labs like the Laboratory for Soft Bioelectronic Interfaces, which can develop state-of-the-art neural electrodes, or labs with access to high-quality patient data.”
As a next step, she is interested in enabling on-chip algorithmic updates to keep up with the evolution of neural signals.
“Neural signals change, and so over time the performance of a neural interface will decline. We are always trying to make algorithms more accurate and reliable, and one way to do that would be to enable on-chip updates, or algorithms that can update themselves.”
Original Article: A neuro-chip to manage brain disorders
More from: École Polytechnique Fédérale de Lausanne
The Latest Updates from Bing News
Go deeper with Bing News on:
AI-powered neural interfaces
- AI-powered wearables that read your thoughts
Meta CEO Mark Zuckerberg is touting an armband that could allow you to type just by thinking. Apple has a patent for airpods that could measure brain activity. At the same time, researchers have ...
- Tie-up to develop AI-powered neural interfaces for people with disabilities
Prometheus has already demonstrated its capabilities in usability tests, allowing users to control an exoskeleton arm through brain signals, facial expressions, or other neurophysiological cues. These ...
- Top Generative AI Tools Every E-commerce Business Should Know
You won’t believe the buzz Generative AI is creating in e-commerce! Imagine crafting personalized content and tailoring experiences, all thanks to this cutting-edge technology. To ace it, you need to ...
- Allianz Trade x Inclusive Brains: AI + Neurotech for Inclusiveness
Paul Barbaste Allianz Trade x Inclusive Brains: AI + Neurotech for Inclusiveness Allianz Trade & Inclusive Brains join forces to foster the inclusion of people with disabilities thanks to AI and ...
- Allianz Trade & Inclusive Brains join forces to foster the inclusion of people with disabilities thanks to AI and neurotechnologies
Allianz Trade, the world’s leading trade credit insurer and Inclusive Brains, a French start-up developing a new generation of neural interfaces powered by generative Artificial Intelligence (AI), ...
Go deeper with Bing News on:
Neuromodulation system-on-chip
- Magnus Medical launches Saint neuromod system for major depressive disorder treatment
Magnus Medical announced the commercial launch of its Saint neuromodulation system for treatment-resistant major depressive disorder (MDD).
- Magnus Medical Announces Commercial Launch of Groundbreaking SAINT Neuromodulation System
“We are thrilled to be the first site in the nation to offer the breakthrough, FDA-cleared SAINT neuromodulation system for individuals suffering from depression, and I am very optimistic that ...
- SiTime launches clock-system-on-a-chip portfolio for AI data center applications
According to the company, its new MEMS (micro-electromechanical system)-based clock-system-on-a-chip (ClkSoC) offerings provide a 10X higher performance when compared to standalone oscillators and ...
- MagVenture, Inc.: MagVenture Announces Collaboration with Magnus Medical to Bring the SAINT® Neuromodulation System to Market
through Magnus' SAINT ® neuromodulation system. SAINT therapy will enable patients to receive a quick, highly effective treatment for Major Depressive Disorder (MDD), especially in the acute ...