{"id":44,"date":"2016-05-09T10:35:03","date_gmt":"2016-05-09T09:35:03","guid":{"rendered":"https:\/\/project.dke.maastrichtuniversity.nl\/brain-robot\/?page_id=44"},"modified":"2016-05-17T09:44:22","modified_gmt":"2016-05-17T08:44:22","slug":"about","status":"publish","type":"page","link":"https:\/\/project.dke.maastrichtuniversity.nl\/brain-robot\/about\/","title":{"rendered":"About"},"content":{"rendered":"<p>The ongoing project started in 2015.<\/p>\n<h3>Description<\/h3>\n<p style=\"text-align: justify;\">The proposed research aims at developing a novel type of interface between human brains and robots.\u00a0Brain-robot interfaces (BRIs) are a subclass of so called brain-machine interfaces (BMIs), that is,\u00a0hardware-software systems that can monitor a person\u2019s brain activity and translate the measured signals\u00a0into commands carried out by an artificial device (a robot in our case, and any kind of machine in\u00a0general). In other words, a BRI makes it possible for a human to control a device to some extent directly\u00a0through intentionally generated brain signals. BRIs\/BMIs have a tremendous range of potential\u00a0applications in various domains such as health care (e.g., support of severely motor-disabled people via\u00a0wheelchair steering and neuroprotheses), augmented human-computer communication, machine-based\u00a0manufacturing, and neuromarketing.<\/p>\n<p style=\"text-align: justify;\">In principle various types of BMIs are thinkable, differing in terms of the implemented functional brain\u00a0imaging method that monitors the neural activation. With respect to BRIs, those functional neuroimaging\u00a0methods are of particular interest that are portable\/mobile and non-invasive. Almost all available BRIs\u00a0are based on (non-)invasive electroencephalography (EEG), yet with none of these neuroelectric BRIs\u00a0going beyond basic laboratory demonstrations. Moreover, a considerable subgroup of users is not able to\u00a0successfully control neuroelectric BMIs. An alternative portable and non-invasive neuroimaging method is\u00a0functional near-infrared spectroscopy (fNIRS). While no fNIRS-based BRI is available today, it obviously\u00a0constitutes a promising alternative to EEG-based BRIs. Next to functional magnetic resonance imaging\u00a0(fMRI), fNIRS belongs to the so-called hemodynamic (vs. neuroelectric) brain imaging. During the last\u00a0decade, fMRI and more recently also fNIRS have been successfully explored for BMI purposes. However,\u00a0so far there exists only a single \u201cproof-of-concept\u201d BRI study implementing brain hemodynamics (fMRI),\u00a0even though hemodynamic brain signals as measured with fNIRS and fMRI have two important\u00a0advantages as compared to EEG: high single-trial reliability and higher spatial resolution. When\u00a0combined with intelligent control paradigms , these advantages can enable the development of effective\u00a0BRI control &#8211; even though hemodynamic signals have a relatively low temporal resolution and are\u00a0detectable only a few seconds after corresponding neuronal events.<\/p>\n<p style=\"text-align: justify;\">Based on these considerations, the proposed research aims at developing a sophisticated brain-robot\u00a0interface based on fNIRS and exploring its benefits and limitations. This interface will be enriched on the\u00a0side of the robot with advanced control methods from artificial intelligence (in particular, machine learning-based self-adaptation and autonomous motion\/grasp planning) so that a human user gets best\u00a0possible support in achieving his\/her intentions through efficient and direct control of a robot via brain\u00a0signals. We will first focus on a basic control setting (reduced command set and elementary tasks) and\u00a0will then iteratively extend this setting by extending the commands and task complexity and successively\u00a0adding state-of-the-art machine-learning and automated-planning techniques. Most of the technical\u00a0equipment needed for our research is already available in our research groups, including a mobile fNIRS\u00a0interface and robots (e.g., turtlebots).<\/p>\n<p style=\"text-align: justify;\">Planned research builds on extensive experience of the applicants in cognitive robotics and neuroscience,\u00a0including, in particular, intelligent robot control and fMRI\/fNIRS-based brain imaging (including BMI).<\/p>\n<h3>Support<\/h3>\n<table style=\"width: 159px;\">\n<tbody>\n<tr style=\"height: 36.875px;\">\n<td style=\"width: 10px; height: 36.875px;\"><a href=\"http:\/\/www.maastrichtuniversity.nl\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter\" src=\"https:\/\/lh5.googleusercontent.com\/-QY3iJXxpxXU\/AAAAAAAAAAI\/AAAAAAAAAts\/bE2JwMqLV0s\/s0-c-k-no-ns\/photo.jpg\" alt=\"Maastricht University\" width=\"1198\" height=\"1198\" \/><\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>This project is co-funded by Maastricht University\u00a0through Luik 3 initiative (Funding for Strategic Innovation).<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The ongoing project started in 2015. 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