NeuralTree, a closed-loop neuromodulation system-on-chip that can detect and alleviate disease symptoms, has been developed by researchers Mahsa Shoaran and Stéphanie Lacour at Swiss Federal Institute of Technology Lausanne (EPFL).
Boasting a 256-channel high-resolution sensing array and an energy-efficient machine learning processor, NeuralTree 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,” said Shoaran. “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.”
NeuralTree functions by extracting neural biomarkers from brain waves. It then, for example, classifies the signals and indicates whether they herald an impending epileptic seizure or Parkinsonian tremor. If a symptom is detected, a neurostimulator — also located on the chip — is activated, sending an electrical pulse to block it.
Shoaran explained that NeuralTree’s unique design gives the system an unprecedented degree of efficiency and versatility compared to its contemporaries. The system can also detect a broader range of symptoms, 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,” said Shoaran.
“Eventually, we can use neural interfaces for many different disorders, and we need algorithmic ideas and advances in chip design to make this happen,” Shoaran added. “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,” she said. “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.”
For more information, contact EPFL at +41 21-693-1111.