\n| ICA (independent component analysis) is one of the most preferred methods for removing motion artifacts from the fNIRS (functional near-infrared spectroscopy) data.\u00a0 In this method, the component derived from a motion artifact is removed by comparing the acceleration sensor data and the signal, which was separated by ICA.\u00a0 However, because of the influence of blood flow, fNIRS data is often delayed in time compared to the acceleration sensor data.\u00a0 For this reason, the correlation is reduced and it is difficult to identify whether the component has been derived from the motion artifact.\u00a0 In this paper, we propose a method to remove the motion artifact using ICA, which takes into account the time delay in fNIRS data.\u00a0 In this proposed method, ICA is executed multiple times, shifting the start time of fNIRS data each time.\u00a0 Then, only the best correlated result is adopted to compare with the acceleration sensor data.\u00a0 In order to examine the effectiveness of the proposed method, the execution results of the proposed method are compared with the results obtained, without considering the time delay.\u00a0 It is found that, the accuracy of removing the motion artifact is improved by the proposed method.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\n2.2. \u8cea\u7591\u5fdc\u7b54
\n\u4eca\u56de\u306e\u8b1b\u6f14\u767a\u8868\u3067\u306f\uff0c\u8cea\u7591\u304c\u3042\u308a\u307e\u305b\u3093\u3067\u3057\u305f\uff0e
\n
\n2.3. \u611f\u60f3
\n\u306f\u3058\u3081\u3066\u306e\u5b66\u4f1a\u3067\u3042\u308a\uff0c\u3055\u3089\u306b\u56fd\u969b\u5b66\u4f1a\u3067\u3042\u3063\u305f\u3053\u3068\u3082\u3042\u308a\uff0c\u3068\u3066\u3082\u7dca\u5f35\u3057\u307e\u3057\u305f\uff0e\u767a\u8868\u306f\uff0c\u6642\u9593\u5185\u306b\u7d42\u308f\u308b\u3053\u3068\u3082\u3067\u304d\u305f\u306e\u3067\uff0c\u3046\u307e\u304f\u767a\u8868\u51fa\u6765\u305f\u306e\u3067\u306f\u306a\u3044\u304b\u3068\u601d\u3044\u307e\u3059\uff0e\u8cea\u554f\u304c\u4f55\u3082\u306a\u304b\u3063\u305f\u306e\u306f\u3068\u3066\u3082\u6b8b\u5ff5\u3067\u3057\u305f\uff0e\u3057\u304b\u3057\uff0c\u30bb\u30c3\u30b7\u30e7\u30f3\u7d42\u4e86\u5f8c\u306b\u8a71\u3057\u304b\u3051\u3066\u3044\u305f\u3060\u3051\u307e\u3057\u305f\uff0e\u307e\u305f\uff0cwelcome reception\u3067\u77e5\u308a\u5408\u3063\u305f\u4eba\u305f\u3061\u304c\u805e\u304d\u306b\u6765\u3066\u304f\u308c\u3066\u3044\u305f\u308a\uff0c\u611f\u60f3\u3092\u805e\u304d\u306b\u6765\u3066\u304f\u308c\u305f\u308a\uff0c\u7814\u7a76\u306b\u3064\u3044\u3066\u30c7\u30a3\u30b9\u30ab\u30c3\u30b7\u30e7\u30f3\u3057\u3088\u3046\u3068\u8a00\u3063\u3066\u3082\u3089\u3048\u305f\u308a\uff0c\u624b\u6cd5\u3084\u8ad6\u6587\u7b49\u3092\u6559\u3048\u3066\u3044\u305f\u3060\u3051\u305f\u308a\u3057\u307e\u3057\u305f\uff0e\u5c71\u4e2d\u6559\u6388\u306e\u8a71\u3092\u306f\u3058\u3081\uff0c\u69d8\u3005\u306a\u7814\u7a76\u767a\u8868\u3084\u8a71\u3092\u805e\u304f\u3053\u3068\u304c\u3067\u304d\uff0c\u5916\u56fd\u306e\u4eba\u3068\u4ef2\u826f\u304f\u306a\u308c\uff0c\u3068\u3066\u3082\u52c9\u5f37\u306b\u306a\u3063\u305f\u3057\uff0c\u697d\u3057\u304f\u5145\u5b9f\u3057\u305f\u6642\u9593\u3092\u904e\u3054\u3059\u3053\u3068\u304c\u3067\u304d\u305f\u3068\u601d\u3044\u307e\u3059\uff0e\u6b21\u56de\u5b66\u4f1a\u306b\u53c2\u52a0\u3059\u308b\u969b\u306b\u306f\u3082\u3046\u5c11\u3057\u82f1\u8a9e\u3092\u8a71\u305b\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u305f\u3044\u3068\u601d\u3044\u307e\u3057\u305f\uff0e
\n
\n3. \u8074\u8b1b
\n\u4eca\u56de\u306e\u8b1b\u6f14\u4f1a\u3067\u306f\uff0c\u4e0b\u8a18\u306e4\u4ef6\u306e\u767a\u8868\u3092\u8074\u8b1b\u3057\u307e\u3057\u305f\uff0e
\n <\/p>\n \n\n\n| \n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aDeveloping Stimulus Presentation on Mobile Devices for a Truly<\/p>\n Portable SSVEP-based BCI<\/p>\n \u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Yu-Te Wang, Yijun Wang, Chung-Kuan Cheng, and Tzyy-Ping Jung<\/p>\n \u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aBrain-computer Interface III<\/p>\n Abstract \uff1a \u00a0This study integrates visual stimulus presentation\u3000and near real-time data processing on a mobile device (e.g. a\u3000Tablet or a cell-phone) to implement a steady-state visual\u3000evoked potentials (SSVEP)-based brain-computer interface\u3000(BCI). The goal of this study is to increase the practicability,\u3000portability and ubiquity of an SSVEP-based BCI for daily use.\u3000The accuracy of flickering frequencies on the mobile SSVEP\u3000BCI system was tested against that on a laptop\/desktop used in\u3000our previous studies. This study then analyzed the power\u3000spectrum density of the electroencephalogram signals elicited by\u3000the visual stimuli rendered on the mobile BCIs. Finally, this\u3000study performed an online test with the Tablet-based BCI\u3000system and obtained an averaged information transfer rate of\u300033.87 bits\/min in three subjects. The current integration leads to\u3000a truly practical and ubiquitous SSVEP BCI on mobile devices\u3000for real-life applications.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u3053\u306e\u7814\u7a76\u3067\u306f\uff0cSSVEP\u30d9\u30fc\u30b9\u306eBCI\u3092\u30e2\u30d0\u30a4\u30eb\u30c7\u30d0\u30a4\u30b9\u4e0a\u3067\u5b9f\u88c5\u3057\uff0c\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u51e6\u7406\u3092\u884c\u3046\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0eBCI\u304c\u8eab\u8fd1\u306a\u30e2\u30d0\u30a4\u30eb\u30c7\u30d0\u30a4\u30b9\u3067\u4f7f\u7528\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308b\u307e\u3067\u767a\u5c55\u3057\u3066\u3044\u308b\u3053\u3068\u306b\u9a5a\u304d\u307e\u3057\u305f\uff0e
\n <\/p>\n \n\n\n| \n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a\u3000Independent Component Analysis of EEG-fMRI data for studying epilepsy and epileptic seizures<\/p>\n \u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Tiziana Franchin, Maria G. Tana, Vittorio Cannat\u00e0, Sergio Cerutti, and Anna M. Bianchi
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Multimodal Blind Source Separation: Algorithms, Applications and Future Challenges
\nAbstruct\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Here we present a method for classifying fMRI independent components (ICs) by using an optimized algorithm for the individuation of noisy signals from sources of interest. The method was applied to estimate brain activations fromcombined EEG\u2010fMRI data for the exploration of epilepsy.\u00a0 Spatial ICA was performed using the above-mentioned optimized algorithm and other three popular algorithms. ICs were sorted considering the value: of the coefficients of determination R2, obtained from the multiple regression analysis with morphometric maps of cerebral matter; of thekurtosis, which features the signal energy. The validation of the method was performed comparing the brain activations obtained with those resulted using the General Linear Model (GLM). The ICA\u2010derived activations in different datasets comprised subareas of the GLM\u2010revealed activations, even if the volume and the shape of activated areas do not correspond exactly. The method proposed also detects additional negative regions implicated in a default mode of brain activity, and not clearly identified by GLM. Compared with a traditional GLM approach, the ICA one provides a flexible way to analyze fMRI data that reduces the assumptions placed upon the hemodynamic response of the brain and the temporal constrains.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u3053\u306e\u767a\u8868\u3067\u306f\uff0c\u3066\u3093\u304b\u3093\u306e\u7814\u7a76\u306e\u305f\u3081\u306bEEG-fMRI\u30c7\u30fc\u30bf\u306bICA\u3092\u7528\u3044\u308b\u3068\u3044\u3046\u3082\u306e\u3067\u3057\u305f\uff0e\u69d8\u3005\u306aICA\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u304c\u7528\u3044\u3089\u308c\u3066\u304a\u308a\uff0c\u305d\u306e\u4e2d\u3067\u3082FastICA\u3068optimized RobustICA\u304c\u826f\u3044\u3068\u3055\u308c\u3066\u3044\u307e\u3057\u305f\uff0e
\n <\/p>\n \n\n\n\u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aCortical Activation Pattern for Grasping During Observation, Imagery, Execution, FES, and Observation-FES Integrated BCI : An Fnirs Pilot Study\u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a An Jinung, Jin SangHyeon, Lee Seung Hyun, Jang GwangHee, Abibullaev Berdakh, Lee Hyun Ju, Moon Jeon Il
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Brain Functional Imaging II
\nAbstract\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a \u00a0Passive movement, action observation and motor imagery as well as motor execution have been suggested to facilitate the motor function of human brain. The purpose of this study is to investigate the cortical activation patterns of these four modes using a functional near-infrared spectroscopy (fNIRS) system. Seven healthy volunteers underwent optical brain imaging by fNIRS. Passive movements were provided by a functional electrical stimulation (FES). Results demonstrated that while all movement modes commonly activated premotor cortex, there were considerable differences between modes. The pattern of neural activation in motor execution was best resembled by passive movement, followed by motor imagery, and lastly by action observation. This result indicates that action observation may be the least preferred way to activate the sensorimotor cortices. Thus, in order to show the feasibility of motor facilitation by a brain computer interface (BCI) for an extreme case, we paradoxically adopted the observation as a control input of the BCI. An observation-FES integrated BCI activated sensorimotor system stronger than observation but slightly weaker than FES. This limitation should be overcome to utilize the observation-FES integrated BCI as an active motor training method.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\u3053\u306e\u7814\u7a76\u3067\u306f\uff0cfNIRS\u88c5\u7f6e\u3092\u5229\u7528\u3057\uff0c\u904b\u52d5\u30a4\u30e1\u30fc\u30b8\u3084FES\u306a\u30694\u7a2e\u985e\u306e\u8133\u306e\u6d3b\u6027\u5316\u30d1\u30bf\u30fc\u30f3\u306e\u691c\u8a0e\u304c\u884c\u308f\u308c\u3066\u3044\u307e\u3057\u305f\uff0eBCI\u3067fNIRS\u304c\u7528\u3044\u3089\u308c\u3066\u3044\u308b\u306e\u306f\u3081\u305a\u3089\u3057\u3044\u3068\u611f\u3058\u307e\u3057\u305f\uff0eEEG\u3067\u306f\u306a\u304f\uff0cfNIRS\u3092\u7528\u3044\u305f\u7406\u7531\u3092\u8cea\u554f\u3059\u308b\u3068\uff0c\u30ce\u30a4\u30ba\u304c\u5c11\u306a\u3044\u304b\u3089\u3068\u7b54\u3048\u3089\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u7cbe\u5ea6\u5411\u4e0a\u304c\u4eca\u5f8c\u306e\u8ab2\u984c\u3060\u305d\u3046\u3067\u3059\uff0e
\n <\/p>\n \n\n\n| \n \u767a\u8868\u30bf\u30a4\u30c8\u30eb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aEvaluation of Classifier Topologies for the Real-time\u3000Classification\u3000of Simultaneous Limb Motions<\/p>\n \u8457\u8005\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1a Max Ortiz-Catalan, Rickard Br\u00e5nemark, and Bo H\u00e5kansson
\n\u30bb\u30c3\u30b7\u30e7\u30f3\u540d\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \uff1aEMG Models and Processing<\/p>\n Abstract \uff1a \u00a0The prediction of motion intent through the\u3000decoding of myoelectric signals has the potential to improve the\u3000functionally of limb prostheses. Considerable research on\u3000individual motion classifiers has been done to exploit this idea.\u3000A drawback with the individual prediction approach, however,\u3000is its limitation to serial control, which is slow, cumbersome,\u3000and unnatural. In this work, different classifier topologies\u3000suitable for the decoding of mixed classes, and thus capable of\u3000predicting simultaneous motions, were investigated in real-time.\u3000These topologies resulted in higher offline accuracies than\u3000previously achieved, but more importantly, positive indications\u3000of their suitability for real-time systems were found.\u3000Furthermore, in order to facilitate further development,\u3000benchmarking, and cooperation, the algorithms and data\u3000generated in this study are freely available as part of\u3000BioPatRec, an open source framework for the development of\u3000advanced prosthetic control strategies.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \u3053\u306e\u7814\u7a76\u3067\u306f\uff0c\u7b4b\u96fb\u4fe1\u53f7\u304b\u3089\u904b\u52d5\u610f\u56f3\u3092\u4e88\u6e2c\u3057\uff0c\u7fa9\u624b\u306e\u3055\u307e\u3056\u307e\u306a\u30d1\u30bf\u30fc\u30f3\u306e\u52d5\u304d\u3092\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u3067\u5236\u5fa1\u3067\u304d\u308b\u305f\u3081\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u304c\u958b\u767a\u3055\u308c\u3066\u3044\u307e\u3057\u305f\uff0e\u5b9f\u969b\u306b\u7fa9\u624b\u304c\u52d5\u3044\u3066\u3044\u308b\u52d5\u753b\u306f\u30b9\u30e0\u30fc\u30ba\u3067\u3059\u3054\u3044\u3068\u611f\u3058\u307e\u3057\u305f\uff0e
\n
\n\u53c2\u8003\u6587\u732e
\n1)\u00a0\u00a0\u00a0 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society in conjunction with 52nd<\/sup> Annual Conference of Japanese Society for Medical and Biological Engineering (JSMBE) , http:\/\/embc2013.embs.org\/<\/a>
\n <\/p>\n","protected":false},"excerpt":{"rendered":"IEEE EMBC2013\u5927\u962a\u4e2d\u4e4b\u5cf6\u306e\u56fd\u969b\u4f1a\u8b70\u5834\u3067\u958b\u50ac\u3055\u308c\u307e\u3057\u305f\u3002 \u7814\u7a76\u5ba4\u304b\u3089\u306f\u3001M1\u306e\u4e2d\u6751\u3055\u3093\u304cICA\u3068\u52a0\u901f\u5ea6\u30bb\u30f3\u30b5\u3092\u7528\u3044\u305ffNIRS\u30c7\u30fc\u30bf\u306b\u5bfe\u3059\u308b\u4f53\u52d5\u9664\u53bb\u624b\u6cd5\u3000\u3068\u3044\u3046\u30bf\u30a4\u30c8\u30eb\u3067\u767a\u8868\u3057\u307e\u3057\u305f\u3002 \u7279\u5225\u8b1b\u6f14\u3067iPS\u7d30\u80de\u306e\u5c71 … <\/p>\n “\u3010\u901f\u5831\u3011\t IEEE EMBC2013” \u306e<\/span>\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10,3],"tags":[],"class_list":["post-1662","post","type-post","status-publish","format-standard","hentry","category-10","category-3"],"_links":{"self":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/1662","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1662"}],"version-history":[{"count":0,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=\/wp\/v2\/posts\/1662\/revisions"}],"wp:attachment":[{"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1662"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1662"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/is.doshisha.ac.jp\/news\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1662"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}} | | | | |