MANAGING BRAIN DISORDERS WITH NEURO-CHIPS

The future for people suffering with brain disorders just became a little brighter thanks in no small part to a discovery made by researchers at the University of Switzerland’s Integrated Neurotechnologies Laboratory.

Combining low-power chip design, machine learning algorithms, and soft implantable electrodes, the team, led by Mahsa Shoaran and Stéphanie Lacour, was able to produce a neural interface which identifies and suppresses symptoms of a range of neurological disorders.

The pair developed a closed-loop neuromodulation system-on-chip that they’ve called NeuralTree which can recognize and address symptoms of disease.

By leveraging a 256-channel high-resolution sensing array and high-powered machine learning processor, a plethora of biomarkers can be extracted and classified from real patient data, leading to increased rates of accuracy when it comes to predicting symptoms before they occur. These neural biomarkers, which are patterns of electrical signals that are understood to be linked to certain neurological disorders, are extracted from brain waves and analyzed to determine whether or not, for instance, a person might be susceptible to an impending epileptic seizure, Parkinsonian tremor, or other type of attack. If any such symptom is detected, a neurostimulator is activated, sending an electrical pulse to block it.

The innovation is state-of-theart, to say the least, and is making waves throughout the brain disorder research community, presenting an incredible opportunity to continue broadening our understanding of brain disorders and improve the ways by which we treat their symptoms.