Miniaturized Brain-Machine Interface (MiBMI)
A new, tiny brain-machine interface (BMI) has been designed that could translate thoughts into words. This development could significantly benefit those with severe motor impairments by allowing them to communicate freely.
Researchers from EPFL have developed this high-performance Miniaturized Brain-Machine Interface (MiBMI) based on silicon chips.
This tiny chip is capable of decoding complex neural signals and converting them into readable text.
“MiBMI allows us to convert intricate neural activity into readable text with high accuracy and low power consumption. This advancement brings us closer to practical, implantable solutions that can significantly enhance communication abilities for individuals with severe motor impairments,” said Mahsa Shoaran of Integrated Neurotechnologies Laboratory (INL) at EPFL’s IEM and Neuro X institutes.
In recent years, research into the usage and development of brain-machine interfaces has been increasing momentum.
These tiny devices hold the potential to help individuals with severe motor disabilities communicate and interact with the world around them.
Elon Musk‘s Neuralink company is actively advancing the development of BMIs. Their goal is to create BMIs that can help people with neurological conditions like paralysis or spinal cord injuries.
As compared to other BMIs, this new MiBMI is compact, efficient, and versatile. Traditional systems are often large, energy-hungry, and have limited use.
To achieve brain-to-text conversion, neural signals produced when a person envisions writing are decoded. Electrodes implanted in the brain capture the neural activity associated with these imagined hand movements.
The MiBMI chipset processes the recorded neural signals instantaneously, transforming the brain’s intended hand movements into digital text.
This device will help enable communication for those with severe motor impairments, including locked-in syndrome.
“While the chip has not yet been integrated into a working BMI, it has processed data from previous live recordings, such as those from the Shenoy lab at Stanford, converting handwriting activity into text with an impressive 91% accuracy,” said Mohammed Ali Shaeri, the lead author.
With its current capability to decode 31 characters, the chip demonstrates immense potential for future advancements.
“We are confident that we can decode up to 100 characters, but a handwriting dataset with more characters is not yet available,” added Shaeri.
The MiBMI’s compact size, low power requirement, and minimal invasiveness make it ideal for implantation.
As a fully integrated system, the MiBMI performs recording and processing on two tiny chips, totaling just 8mm2.
“Its minimal invasiveness ensures safety and practicality for use in clinical and real-life settings,” the press release noted.
The MiBMI chip breakthrough design is a game-changer for future brain-machine interfaces. It makes practical, fully implantable devices a reality.
This has the potential to greatly improve the quality of life for patients with amyotrophic lateral sclerosis (ALS) and spinal cord injuries, allowing them to communicate without the need for physical movement.
“Our goal is to develop a versatile BMI that can be tailored to various neurological disorders, providing a broader range of solutions for patients,” added Shoaran.
The team presented the development at the International Solid-State Circuits Conference.
The findings were reported in the IEEE Journal of Solid-State Circuits.
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Mrigakshi Dixit Mrigakshi is a science journalist who enjoys writing about space exploration, biology, and technological innovations. Her work has been featured in well-known publications including Nature India, Supercluster, The Weather Channel and Astronomy magazine. If you have pitches in mind, please do not hesitate to email her.
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