Tens of millions of people suffer from Parkinson’s disease and epilepsy. The resulting human and financial cost of is staggering.

Fortunately, researchers have now been able to combine low-power chip design, machine learning algorithms, and soft implantable electrodes to produce NeuralTree, a closed-loop neuromodulation system-on-a-chip that can detect and alleviate symptoms of Parkinson’s disease and epilepsy.

NeuralTree benefits from the accuracy of a neural network and the hardware efficiency of a decision tree algorithm. It’s the first time we’ve been able to integrate such a complex, yet energy-efficient neural interface for seizure or tremor detection, as well as for multiclass tasks such as finger movement classification for neuro-prosthetic applications.

The results of this research were presented at the 2022 IEEE International Solid-State Circuits Conference and published in the IEEE Journal of Solid-State Circuits.

NeuralTree functions by extracting neural biomarkers from brain waves. It then 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. This is the first demonstration of Parkinsonian tremor detection with an on-chip classifier.

The researchers explain 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; this allows more high-resolution data to be processed on the implant.

 

The chip’s extremely small size gives it great potential for scalability to more channels. And the integration of an ‘energy-aware’ learning algorithm makes NeuralTree highly energy efficient.

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.

As a next step, the team is interested in enabling on-chip algorithmic updates to keep up with the evolution of neural signals. That’s important because neural signals change; so, over time the performance of a neural interface will decline unless updated. One way to address that is to enable on-chip updates, or algorithms that can update themselves.