Podcast: The Future of Neurology: Neural Interfaces Countering Neural Diseases

Daniel Mitchell

fromThe Next Byte

21 Feb, 2023

In this episode, we talk about NeuralTree: a neural interface capable of detecting neural impulses associated with brain disorders and countering them.

brain–machine interface (BMIs)

- healthcare

- machine learning

In this episode, we talk about NeuralTree: a neural interface capable of detecting neural impulses associated with brain disorders and countering them.

EPISODE NOTES

(0:46) - A Neuro-chip To Manage Brain Disorders

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A neuro-chip to manage brain disorders

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.

Celia Luterbacher

fromEPFL

31 Jan, 2023

brain–machine interface (BMIs)

- healthcare

- machine learning

This article was discussed in our Next Byte podcast.

The full article will continue below.

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 a

Image: EPFL and Nissan researchers are able to read a driver’s brain signals and send them to a smart vehicle so that it can anticipate the driver’s moves and facilitate the driving process. Nissan recently unveiled this brain-to-vehicle (B2V) technology.

A smart car that can read brain signals

Future cars will be both self-driving and manual. "We wanted to harness technology to enhance drivers’ skills without interfering with the enjoyment of being behind the wheel," explains José del R. Millán, who holds the Defitech Foundation Chair in Brain-Machine Interface (CNBI).

Clara Marc

fromEPFL

10 Oct, 2018

brain–machine interface (BMIs)

- machine learning

Future cars will be both self-driving and manual. "We wanted to harness technology to enhance drivers’ skills without interfering with the enjoyment of being behind the wheel," explains José del R. Millán, who holds the Defitech Foundation Chair in Brain-Machine Interface (CNBI). As part of a joint project with Nissan researchers based at the CNBI, the team managed to read the brain signals that indicate a driver is about to do something – such as accelerate, brake or change lanes – in order to send that information to the vehicle. With this advance notice of a few hundreds of milliseconds, the smart vehicle can anticipate the driver's movements and make the ensuing maneuver easier. The car is also equipped with sensors to monitor its environment, which means it can help the driver when traffic conditions are difficult.

An easier and more personalized driving experience

The signals produced in the driver's frontal motor cortex are detected using a sensor-equipped EEG (electroencephalography) headset. They are then sent to the smart vehicle for processing. By combining that data with the information detected by its own sensors, the car can react to the situation at hand. "If you're coming to a red light and getting ready to brake, the car will assist you by starting to brake 200–500 milliseconds before you do. But if you approach a red light and your brain shows no intention of slowing the car down, the car will warn you that the light is red to make sure you've seen it," says Millán.

We all generate different patterns of brain signals, so the vehicle learns from each driver and customizes its software. It stores each driver's regular routes, as well as their driving habits and style, using this information to more accurately anticipates what each driver might do at any time. The brain-machine interface not only makes driving easier, it also creates a more personalized experience, as the car will always be in sync with the

Podcast: The Future of Neurology: Neural Interfaces Countering Neural Diseases

In this episode, we talk about NeuralTree: a neural interface capable of detecting neural impulses associated with brain disorders and countering them.

EPISODE NOTES

Efficiency, scalability, and versatility

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In this episode, we talk about NeuralTree: a neural interface capable of detecting neural impulses associated with brain disorders and countering them.

EPISODE NOTES

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