{"id":4620,"date":"2009-07-24T00:01:47","date_gmt":"2009-07-24T00:01:47","guid":{"rendered":"http:\/\/www.physiologicalcomputing.net\/wordpress\/?p=219"},"modified":"2021-12-22T20:22:10","modified_gmt":"2021-12-22T20:22:10","slug":"mindflex-force-trainer-at-san-diego-comic-con-2","status":"publish","type":"post","link":"https:\/\/www.physiologicalcomputing.net\/?p=4620","title":{"rendered":"Mindflex &#038; Force Trainer at San Diego Comic-Con"},"content":{"rendered":"<p>If your in the vicinity of San Diego this week be sure to check out Mattel&#8217;s MindFlex and Uncle Milton&#8217;s Force Trainer at the Sand Diego Comic-Con (23\/7-26\/7). MindFlex and Force Trainer are brain wave controlled toys developed using <a href=\"http:\/\/company.neurosky.com\/\">Neurosky&#8217;s<\/a> BCI headset + development platform. Each toy implements a simple BCI mechanic: leviatate a ball by regulating brain activity. MindFlex uses frontal theta* brainwaves to control the ball which is known to increase with attention \/ mental effort (e.g. focus your attention on the ball to make it rise). I imagine the same signal is used for Force Trainer though I&#8217;ve yet to confirm this. The simplicity of such a mechanic should make it relatively easy for the casual user to play with the toy without any training in modulating their braiwaves.<\/p>\n<p>You can find each demo at booths #3029 (MindFlex) and #2913U (Force Trainer)<\/p>\n<p>Both products are due out later this year.<\/p>\n<p>* Based on descriptions of the product and the placement of the EEG sensor.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>If your in the vicinity of San Diego this week be sure to check out Mattel&#8217;s MindFlex and Uncle Milton&#8217;s Force Trainer at the Sand Diego Comic-Con (23\/7-26\/7). MindFlex and Force Trainer are brain wave controlled toys developed using Neurosky&#8217;s BCI headset + development platform. Each toy implements a simple BCI mechanic: leviatate a ball [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"spay_email":""},"categories":[6],"tags":[18,30,53,54,75],"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/pY315-1cw","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts\/4620"}],"collection":[{"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4620"}],"version-history":[{"count":1,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts\/4620\/revisions"}],"predecessor-version":[{"id":4748,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=\/wp\/v2\/posts\/4620\/revisions\/4748"}],"wp:attachment":[{"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4620"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4620"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.physiologicalcomputing.net\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4620"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}