{"id":87,"date":"2016-04-20T14:38:39","date_gmt":"2016-04-20T14:38:39","guid":{"rendered":"https:\/\/blogs.mathworks.com\/headlines\/?p=87"},"modified":"2023-12-22T10:35:54","modified_gmt":"2023-12-22T15:35:54","slug":"neuroscience-and-machine-learning-restore-movement-in-paralyzed-mans-hand","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/headlines\/2016\/04\/20\/neuroscience-and-machine-learning-restore-movement-in-paralyzed-mans-hand\/","title":{"rendered":"Neuroscience and Machine Learning Restore Movement in Paralyzed Man\u2019s Hand"},"content":{"rendered":"

Last week, the New York Times<\/a> reported the first successful \u201climb reanimation\u201d in a person with quadriplegia. Ian Burkhart, 24, had broken his neck as a teen in a diving accident. His spine was damaged at the fifth cervical vertebra, leaving him paralyzed from the shoulders down.\u00a0Using nerve bypass technology that transmits his thoughts directly to his hand muscles, he has regained control over his right hand and fingers.<\/p>\n

The system effectively bypasses his spinal injury. This is the first time a brain-computer interface has been used to help an individual move his own hands.\u00a0The research was published in in the journal Nature<\/a>, and explains how machine learning <\/a>and MATLAB <\/a>were used in this project.<\/p>\n

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Image Credit: BBC. Source: Battelle\/Ohio State University<\/p><\/div><\/p>\n

This remarkable breakthrough is the result of years of research and a cross-discipline project. Engineers, bioengineers, neuroscientists, and surgeons contributed to the prototype medical system and clinical study.\u00a0The device, called NeuroLife<\/a>, was invented at Battelle Institute<\/a>. The engineers at Battelle worked with physicians and neuroscientists from Ohio State University Wexner Medical Center<\/a> to develop the research approach and perform the clinical study.<\/p>\n

In 2014, Ohio State surgeons implanted a chip in Ian\u2019s brain. The Utah Array chip<\/a>\u00a0from Blackrock Microsystems<\/a>\u00a0consists of 96 microelectrode sensors that record the firing of individual neurons.\u00a0The team used brain imaging to identify and isolate the part of Mr. Burkhart\u2019s brain that controls hand movements. During the surgery, the team repeatedly tested exposed brain tissue to pinpoint the correct location for the chip.<\/p>\n

After the surgery, Mr. Burkhart spent hours upon hours watching an avatar of a hand on a screen, concentrating on the thoughts it would take to make the movement.\u00a0 During these sessions, the activity in his brain was recorded and analyzed.\u00a0The goal was to understand which signals in his brain corresponded with the hand movements he was mentally emulating.<\/p>\n

The firing patterns in his brain were transmitted to a computer and recorded. The amount of data this requires is immense. Three million samples of neural activity are collected each second. \u00a0Approximately\u00a0one\u00a0gigabyte of data is generated from neurons in the motor cortex\u00a0every three minutes.<\/p>\n

The researchers used machine learning\u00a0to decode these patterns. Through repetition, the firing patterns were analyzed and used to develop an algorithm to control the muscles in his hand.\u00a0The algorithm was adjusted after almost every training session. Machine learning aided the\u00a0recalibration as Ian’s\u00a0mind adjusted to the movement and apparatus.<\/p>\n

How MATLAB was used in this project<\/strong><\/h2>\n

From the journal Nature:<\/p>\n

“The digitized data were then transmitted to a personal computer where they were decoded to determine which motion was being imagined and then encoded to evoke the desired response from the muscles in the forearm. The computer communicated with the custom high-definition neuromuscular electrical stimulator (NMES) that drove the electrode sleeve wrapped around the forearm.<\/span><\/p>\n

“The signal processing<\/a><\/span> and decoding\/control algorithms were all run on a personal computer using MATLAB. The digitized data from the Neuroport was processed every 100 ms.”<\/span><\/p>\n

The computer decoded the signals from the brain and translated them to specific movements. \u00a0The team used a NMES sleeve that applied electrical stimulus to his hand muscles. \u00a0The sleeve has 130 electrodes that rest on the skin\u2019s surface and transmit signals through Ian\u2019s arm to activate his muscles.<\/p>\n

After a year of training, Ian was able to learn through repetition to pour from a bottle, and stir a cup.\u00a0 He was also able to learn to play a guitar video game!<\/p>\n

More work is needed<\/strong><\/h2>\n