3D model of the human brain on dark background surrounded by neural networks. 3d render. 3d illustration Synapses and neurons

Brain chip allows paralysed man to ‘write’ on screen

Image credit: Anton Chervov/Dreamstime

Researchers in the US have developed a method of communication for people with paralysis that uses a computer to turn mental handwriting into on-screen words.

As part of a new study, a paralysed man used his brain to write on a screen at speeds almost as fast as an able-bodied adult texting on a smartphone, its developers at Stanford University said. A computer decodes attempted handwriting movements from brain signals, and may allow much faster communication than was previously possible.

“This approach allowed a person with paralysis to compose sentences at speeds nearly comparable to those of able-bodied adults of the same age typing on a smartphone,” said Jaimie Henderson, professor of neurosurgery. “The goal is to restore the ability to communicate by text.”

The investigators coupled artificial intelligence (AI) software with a device, called a brain-computer interface (BCI), implanted in the man’s brain. The software could decode information from the computer to convert the man’s thoughts about handwriting into text on a computer screen, according to the study.

Using this approach, the man was able to write more than twice as quickly as he could using a previous method developed by the Stanford researchers, who reported those findings in 2017 in the journal eLife.

Researchers said the new findings could lead to further advances benefitting millions of people globally, who have lost the use of their upper limbs or their ability to speak due to spinal cord injuries, strokes, or amyotrophic lateral sclerosis, also known as Lou Gehrig’s disease.

“Just think about how much of your day is spent on a computer or communicating with another person,” added study co-author Krishna Shenoy. “Restoring the ability of people who have lost their independence to interact with computers and others is extremely important, and that is what is bringing projects like this one front and centre.”

Undated handout image issued by the Howard Hughes Medical Institute of a brain computer interface turning mental handwriting into text on a screen.

An image issued by the Howard Hughes Medical Institute of a brain-computer interface turning mental handwriting into text on a screen.

Image credit: F. Willett et al./Nature 2021/Erika Woodrum

According to the scientists, the participant was able to reach a writing speed of about 18 words per minute with 94.1 per cent accuracy. By comparison, able-bodied people of the same age can punch out about 23 words per minute on a smartphone.

The researchers instructed him to write sentences as if his hand was not paralysed, by imagining that he was holding a pen on a piece of ruled paper. During this exercise, the BCI used a neural network – a type of machine learning – to translate attempted handwriting movements from neural activity into text in real-time.

The participant, referred to as T5, lost practically all movement below the neck because of a spinal cord injury in 2007. Nine years later, Dr Henderson placed two brain-computer interface chips, each the size of a baby aspirin, on the left side of his brain.

Each chip in his brain has 100 electrodes that pick up signals from neurons firing in the part of the motor cortex – a region of the brain’s outermost surface – that governs hand movement. The experts then sent those neural signals via wires to a computer, where AI algorithms decode the signals and surmise T5’s intended hand and finger motion.

“This method is a marked improvement over existing communication BCIs that rely on using the brain to move a cursor to ‘type’ words on a screen,” said lead author Frank Willett. “Attempting to write each letter produces a unique pattern of activity in the brain, making it easier for the computer to identify what is being written with much greater accuracy and speed.”

Experts say further demonstrations of longevity, safety and efficacy are required before the method can be used clinically. The researchers suggest these methods could be applied more generally to any sequential behaviour that cannot be observed directly, such as decoding speech from someone who can no longer speak.

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