{"id":96,"date":"2018-06-13T09:39:44","date_gmt":"2018-06-13T00:39:44","guid":{"rendered":"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/?page_id=96"},"modified":"2024-06-28T14:33:15","modified_gmt":"2024-06-28T05:33:15","slug":"en-introduction","status":"publish","type":"page","link":"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/en-introduction\/","title":{"rendered":"Our Research"},"content":{"rendered":"<h3>1) Development and clinical application of BMI using ECoG\/MEG<\/h3>\n<p class=\"margin_B20\"><span style=\"font-family: 'arial black', sans-serif;\">A brain-machine interface (BMI) is a system that decodes brain activity to control an external device such as a robotic arm, PC or communication device. The BMI enables a severely paralyzed patient to restore motor function and communication ability through neuroprosthesis. This technology allows communication even for patients without any muscle activity (i.e., those in a totally locked-in state), making BMI one of the biggest hopes for such patients.<\/span><br \/>\n<span style=\"font-family: 'arial black', sans-serif;\">We have developed the BMI using human electroencephalographic (ECoG) signals recorded by intracranial electrodes placed on the surface of the human brain (Figure 1) and magnetoencephalography (MEG; Figure 2).<\/span><\/p>\n<div class=\"align_C margin_B20\">\n<table style=\"height: 312px; width: 59.8277%; border-collapse: collapse; border-style: hidden; background-color: #336380;\">\n<tbody>\n<tr>\n<td style=\"width: 100%;\"><span style=\"font-family: 'arial black', sans-serif;\"><img loading=\"lazy\" class=\"\" src=\"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-content\/uploads\/2018\/06\/research_img01.png\" alt=\"Figure 1: ECoG recording\" width=\"317\" height=\"267\" \/><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-family: 'arial black', sans-serif;\"><strong>Figure 1: ECoG recording<\/strong><\/span><\/p>\n<\/div>\n<div class=\"align_C margin_B40\">\n<table style=\"height: 273px; width: 106.724%; border-collapse: collapse; border-style: hidden; background-color: #336380;\">\n<tbody>\n<tr style=\"height: 273px;\">\n<td style=\"width: 100%; height: 273px;\"><span style=\"font-family: 'arial black', sans-serif;\"><img loading=\"lazy\" class=\"\" src=\"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-content\/uploads\/2018\/06\/research_img02.png\" alt=\"Figure 2: MEG recording\" width=\"687\" height=\"264\" \/><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-family: 'arial black', sans-serif;\"><strong>Figure 2: MEG recording<\/strong><\/span><\/p>\n<\/div>\n<p><span style=\"font-family: 'arial black', sans-serif;\"><span lang=\"EN-US\">Video 1 shows a robotic hand controlled by the BMI using ECoG signals. The desired hand movements of the subject implanted with the electrodes were successfully decoded from the online ECoG signals. We have also developed a robotic hand controlled by MEG signals<\/span>\uff08<span lang=\"EN-US\"><a href=\"https:\/\/media.nature.com\/original\/nature-assets\/srep\/2016\/160224\/srep21781\/extref\/srep21781-s2.mov\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/media.nature.com\/original\/nature-assets\/srep\/2016\/160224\/srep21781\/extref\/srep21781-s2.mov<\/a>).\u00a0<\/span><\/span><\/p>\n<div class=\"research_movie_box\">\n<table style=\"height: 362px; width: 100%; border-collapse: collapse; background-color: #336380;\">\n<tbody>\n<tr style=\"height: 362px;\">\n<td style=\"width: 100%; height: 362px;\"><span style=\"font-family: 'arial black', sans-serif;\"><video id=\"video\" controls=\"controls\" width=\"300\" height=\"150\"><source src=\"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-content\/uploads\/2018\/06\/robot_selec01.mp4\" type=\"video\/mp4\" \/><embed src=\"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-content\/uploads\/2018\/06\/robot_selec01.mp4\" width=\"100%\" height=\"auto\" type=\"video\/mp4\" autoplay=\"autoplay\" controller=\"true\" pluginspage=\"http:\/\/www.apple.com\/jp\/quicktime\/download\/\" \/><\/video><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div><\/div>\n<div class=\"research_movie_box\"><span style=\"font-family: 'arial black', sans-serif;\"><strong>Video 1. Left: The left hand of the patient recording ECoG signals; Right: The robotic hand controlled by BMI using online ECoG signals.<\/strong><\/span><\/div>\n<div><\/div>\n<div>\n<p><span style=\"font-family: 'arial black', sans-serif;\"><span lang=\"EN-US\">Also, we are developing a novel BCI using ECoG in the project of <\/span><span lang=\"EN-US\">JST CREST [Symbiotic interaction] Creation and development of core technologies interfacing human and information environments,\u00a0Construction of representational Brain-Computer Interaction technology\u00a0(<\/span><span lang=\"EN-US\"><a href=\"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/CREST\/en\/\">website<\/a>)<\/span><span lang=\"EN-US\">.<\/span><\/span><\/p>\n<table style=\"height: 347px; width: 100%; border-collapse: collapse; background-color: #336380;\">\n<tbody>\n<tr style=\"height: 347px;\">\n<td style=\"width: 100%; height: 347px;\"><span style=\"font-family: 'arial black', sans-serif;\"><img loading=\"lazy\" class=\"alignnone size-large wp-image-485\" src=\"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-content\/uploads\/2019\/10\/TOP_CREST1-1024x439.png\" alt=\"\" width=\"1024\" height=\"439\" srcset=\"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-content\/uploads\/2019\/10\/TOP_CREST1-1024x439.png 1024w, https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-content\/uploads\/2019\/10\/TOP_CREST1-300x129.png 300w, https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-content\/uploads\/2019\/10\/TOP_CREST1-768x329.png 768w, https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-content\/uploads\/2019\/10\/TOP_CREST1-670x287.png 670w, https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-content\/uploads\/2019\/10\/TOP_CREST1.png 1400w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<\/div>\n<p><span style=\"font-family: 'arial black', sans-serif;\">Our task is to improve the BMI so that it is suitable for clinical application.<\/span><\/p>\n<h3><span style=\"font-family: 'arial black', sans-serif;\">2) BMI neurofeedback to understand and treat neurological diseases<\/span><\/h3>\n<p class=\"margin_B20\"><span style=\"font-family: 'arial black', sans-serif;\">BMI is a useful tool to induce plastic alteration of the cortical representations to modulate neurological diseases. We applied MEG-based BMI for patients with phantom limb pain, who suffer intractable pain after amputation or other severe peripheral nerve damage\uff08<a href=\"https:\/\/media.nature.com\/original\/nature-assets\/ncomms\/2016\/161027\/ncomms13209\/extref\/ncomms13209-s2.mov\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/media.nature.com\/original\/nature-assets\/ncomms\/2016\/161027\/ncomms13209\/extref\/ncomms13209-s2.mov<\/a>\uff09. We found that if a patient tried to control a robotic hand using the BMI and associating the movement with their missing arm, it increased their pain, but training the patients to associate the movement of the prosthetic with the unaffected hand decreased their pain (Figure 3). These results demonstrated that BMI training could control phantom limb pain depending on the induced sensorimotor plasticity. We are applying the BMI neurofeedback technique to various neurological diseases to understand the neural mechanism of the diseases and to treat their symptoms.<\/span><\/p>\n<table style=\"height: 357px; width: 100%; border-collapse: collapse; background-color: #336380;\">\n<tbody>\n<tr style=\"height: 357px;\">\n<td style=\"width: 100%; height: 357px;\"><span style=\"font-family: 'arial black', sans-serif;\"><img src=\"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-content\/uploads\/2018\/06\/research_img04_en.png\" alt=\"Figure 3: BMI neurofeedback for phantom limb pain\" \/><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<div class=\"align_C margin_B40\"><span style=\"font-family: 'arial black', sans-serif;\"><strong>Figure 3: BMI neurofeedback for phantom limb pain<\/strong><\/span><\/div>\n<div>\n<p><span style=\"font-family: 'arial black', sans-serif;\">We reported that signals measured in the subthalamic nucleus could be modulated by the patient&#8217;s intention, altering deep brain activity.<\/span><\/p>\n<p><span style=\"font-family: 'arial black', sans-serif;\"><span style=\"float: none; background-color: #ffffff; color: #333333; cursor: text; font-size: 13px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: 1.3px; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px; display: inline !important;\">Details of the dissertation<\/span>\uff08Publishd\uff1a<span lang=\"EN-US\" style=\"color: black; background: white;\">17 December 2018<\/span>\uff09<\/span><\/p>\n<p><span style=\"font-family: 'arial black', sans-serif;\"><span lang=\"EN-US\">Real-time neurofeedback to modulate <\/span>\u03b2<span lang=\"EN-US\">-band power in the subthalamic nucleus in Parkinson<\/span>\u2019<span lang=\"EN-US\">s disease patients<\/span><\/span><\/p>\n<p><span lang=\"EN-US\" style=\"font-family: 'arial black', sans-serif;\"><a href=\"http:\/\/www.eneuro.org\/content\/early\/2018\/12\/14\/ENEURO.0246-18.2018\" target=\"_blank\" rel=\"noopener noreferrer\">http:\/\/www.eneuro.org\/content\/early\/2018\/12\/14\/ENEURO.0246-18.2018<\/a><\/span><\/p>\n<p><span lang=\"EN-US\" style=\"font-family: 'arial black', sans-serif;\"><a href=\"https:\/\/medicalxpress.com\/news\/2018-12-real-time-feedback-parkinson-brainwaves.html\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/medicalxpress.com\/news\/2018-12-real-time-feedback-parkinson-brainwaves.html<\/a><\/span><\/p>\n<\/div>\n<p>&nbsp;<\/p>\n<p><span lang=\"EN-US\" style=\"font-family: 'arial black', sans-serif;\">We are involving the JST ERATO Ikegaya Brain-AI Hybrid project that aims to develop a novel human ability through neurofeedback based on decoding of neural information (<a href=\"http:\/\/www.ikegaya.jp\/ERATO\/index.html\">Web page here<\/a>).<\/span><\/p>\n<p>&nbsp;<\/p>\n<div><\/div>\n<h3><span style=\"font-family: 'arial black', sans-serif;\">3) Computer-aided diagnosis of neurological diseases using MEG\/EEG<\/span><\/h3>\n<p><span style=\"font-family: 'arial black', sans-serif;\">We are developing a novel computer-aided diagnostic tool using artificial intelligence with big data from MEG\/EEG signals.<\/span><\/p>\n<div><span style=\"font-family: 'arial black', sans-serif;\">By analyzing the waveforms of EEG and MEG, we can find the features of various diseases. We have previously identified that phase-amplitude coupling is characteristically found in cortical EEG during epileptic seizures. Also, we are currently researching automatically diagnosing diseases and finding new features by using artificial intelligence technology on the big data of our brain waves and magnetoencephalograms.<\/span><\/div>\n<div><span style=\"font-family: 'arial black', sans-serif;\">We have developed a neural network to identify epilepsy, spinal cord injury, and healthy people from resting magnetoencephalographic signals.<\/span><\/div>\n<div>\n<p><span lang=\"EN-US\" style=\"font-family: 'arial black', sans-serif;\"><a href=\"http:\/\/resou.osaka-u.ac.jp\/ja\/research\/2019\/20190326_1\" target=\"_blank\" rel=\"noopener noreferrer\">http:\/\/resou.osaka-u.ac.jp\/ja\/research\/2019\/20190326_1<\/a><\/span><\/p>\n<p><span style=\"font-family: 'arial black', sans-serif;\">Details of the dissertation\uff08<a href=\"https:\/\/www.nature.com\/articles\/s41598-019-41500-x\/figures\/#article-info\">Published: <time datetime=\"2019-03-25\">25 March 2019<\/time><\/a>\uff09<\/span><\/p>\n<p><span lang=\"EN-US\" style=\"font-family: 'arial black', sans-serif;\"><a href=\"https:\/\/www.nature.com\/articles\/s41598-019-41500-x\/figures\/\" target=\"_blank\" rel=\"noopener noreferrer\">Automatic diagnosis of neurological diseases using MEG signals with a deep neural network<\/a><\/span><\/p>\n<p><span lang=\"EN-US\" style=\"font-family: 'arial black', sans-serif;\">Original code;\u00a0<a href=\"https:\/\/github.com\/yanagisawa-lab\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/github.com\/yanagisawa-lab<\/a><\/span><\/p>\n<\/div>\n<p>&nbsp;<\/p>\n<p><span style=\"font-family: 'arial black', sans-serif;\"><strong>Visualization and manipulation of our perception and imagery by BCI by Takufumi Yanagisawa<\/strong><\/span><\/p>\n<p><iframe loading=\"lazy\" title=\"Visualization and manipulation of our perception and imagery by BCI by Takufumi Yanagisawa\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/UCNaaHfDeOc?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n","protected":false},"excerpt":{"rendered":"<p>1) Development and clinical application of BMI using ECoG\/MEG A brain-machine interface (BMI) is a system that [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1398,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-json\/wp\/v2\/pages\/96"}],"collection":[{"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-json\/wp\/v2\/comments?post=96"}],"version-history":[{"count":18,"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-json\/wp\/v2\/pages\/96\/revisions"}],"predecessor-version":[{"id":1502,"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-json\/wp\/v2\/pages\/96\/revisions\/1502"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-json\/wp\/v2\/media\/1398"}],"wp:attachment":[{"href":"https:\/\/www.med.osaka-u.ac.jp\/pub\/nsurg\/yanagisawa\/wp-json\/wp\/v2\/media?parent=96"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}