{"id":1851,"date":"2018-09-25T13:36:49","date_gmt":"2018-09-25T04:36:49","guid":{"rendered":"http:\/\/163.180.4.222\/lab\/?p=1851"},"modified":"2018-09-25T13:37:10","modified_gmt":"2018-09-25T04:37:10","slug":"meeting-brain-computer-interface-user-performance-expectations-using-a-deep-neural-network-decoding-framework","status":"publish","type":"post","link":"https:\/\/biochemistry.khu.ac.kr\/lab\/?p=1851","title":{"rendered":"Meeting brain\u2013computer interface user performance expectations using a deep neural network decoding framework"},"content":{"rendered":"

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\uc5ec\uae30<\/u><\/a>\ub97c \ud074\ub9ad\ud558\uc138\uc694~<\/p>\n

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Abstract<\/h6>\n
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Brain\u2013computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control of assistive devices1<\/a>,2<\/a>,3<\/a>,4<\/a>,5<\/a>,6<\/a>,7<\/a>,8<\/a>,9<\/a><\/sup>. Surveys of potential end-users have identified key BCI system features10<\/a>,11<\/a>,12<\/a>,13<\/a>,14<\/a><\/sup>, including high accuracy, minimal daily setup, rapid response times, and multifunctionality. These performance characteristics are primarily influenced by the BCI\u2019s neural decoding algorithm1<\/a>,15<\/a><\/sup>, which is trained to associate neural activation patterns with intended user actions. Here, we introduce a new deep neural network16<\/a><\/sup>\u00a0decoding framework for BCI systems enabling discrete movements that addresses these four key performance characteristics. Using intracortical data from a participant with tetraplegia, we provide offline results demonstrating that our decoder is highly accurate, sustains this performance beyond a year without explicit daily retraining by combining it with an unsupervised updating procedure3<\/a>,17<\/a>,18<\/a>,19<\/a>,20<\/a><\/sup>, responds faster than competing methods8<\/a><\/sup>, and can increase functionality with minimal retraining by using a technique known as transfer learning21<\/a><\/sup>. We then show that our participant can use the decoder in real-time to reanimate his paralyzed forearm with functional electrical stimulation (FES), enabling accurate manipulation of three objects from the grasp and release test (GRT)22<\/a><\/sup>. These results demonstrate that deep neural network decoders can advance the clinical translation of BCI technology.<\/p>\n<\/div>\n

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    \uc5ec\uae30\ub97c \ud074\ub9ad\ud558\uc138\uc694~   Abstract Brain\u2013computer interface (BCI) neurotechnology has the potential to reduce disability associated with paralysis by translating neural activity into control(more…)<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[33,35,29,30],"tags":[],"class_list":["post-1851","post","type-post","status-publish","format-standard","hentry","category-do-biology","category-lets-do-computer-science","category-lets-do-science","category-recent-science-news"],"aioseo_notices":[],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack-related-posts":[{"id":3419,"url":"https:\/\/biochemistry.khu.ac.kr\/lab\/?p=3419","url_meta":{"origin":1851,"position":0},"title":"Brain 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