New brain signal decoding technology can answer emotional changes
New brain signal decoding technology can answer emotional changes
September 12, 2018 Source: Science and Technology Daily Author: room Linlin
Window._bd_share_config={ "common":{ "bdSnsKey":{ },"bdText":"","bdMini":"2","bdMiniList":false,"bdPic":"","bdStyle":" 0","bdSize":"16"},"share":{ }};with(document)0[(getElementsByTagName('head')[0]||body).appendChild(createElement('script')) .src='http://bdimg.share.baidu.com/static/api/js/share.js?v=89860593.js?cdnversion='+~(-new Date()/36e5)];A team of engineers and doctors at the University of Southern California and the University of California at San Francisco found that they can learn how to change moods from the decoding of neural signals by developing a new decoding technique. The new study was published in the latest issue of Nature Biotechnology.
This is an important step towards a new closed-loop therapy that is beneficial for patients with depression and anxiety using brain stimulation.
Researchers are developing new biomedical technologies for the treatment of refractory neurological diseases under the SUBNETS program of the US Department of Defense's Advanced Research Projects Agency. The team recruited seven patients with epilepsy and inserted intracranial electrodes in their brains for standard clinical testing to determine their seizures. At the same time, the team asked the subjects to answer the questionnaire on the tablet and “evaluate the feelings of the present†to understand their intermittent emotional changes. The score has 7 grades, the higher the more positive the mental state.
Using a large amount of brain signal data recorded by electrodes, the team developed a new decoding technique that can simultaneously process neural signals from distributed brains.
In this way, researchers can find patterns of brain signals that match the patient's self-reported mood. Using these knowledge-constructed decoders, it is possible to independently identify the signal patterns corresponding to a certain emotion, and then predict the emotional changes of each patient over many days.
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