{"id":4007,"date":"2025-10-14T13:32:33","date_gmt":"2025-10-14T13:32:33","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4007"},"modified":"2025-10-14T13:32:33","modified_gmt":"2025-10-14T13:32:33","slug":"bayesian-filtered-fmri-streams-for-rf-control-loops","status":"publish","type":"page","link":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/?page_id=4007","title":{"rendered":"Bayesian-Filtered fMRI Streams for RF Control Loops"},"content":{"rendered":"\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/Bayesian-Filtered-fMRI-Streams-for-RF-Control-Loops-bgilbert2.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of Bayesian-Filtered fMRI Streams for RF Control Loops bgilbert2.\"><\/object><a id=\"wp-block-file--media-3c4b699a-ff87-400c-8008-be4e828054a2\" href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/Bayesian-Filtered-fMRI-Streams-for-RF-Control-Loops-bgilbert2.pdf\">Bayesian-Filtered fMRI Streams for RF Control Loops bgilbert2<\/a><a href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/Bayesian-Filtered-fMRI-Streams-for-RF-Control-Loops-bgilbert2.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-3c4b699a-ff87-400c-8008-be4e828054a2\">Download<\/a><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">This paper presents a novel approach for filtering<br>functional Magnetic Resonance Imaging (fMRI) data streams<br>using Bayesian techniques, specifically designed for real-time<br>Radio Frequency (RF) control loops. We implement and compare<br>two primary filtering methods: causal Kalman filtering for realtime applications and non-causal Gaussian smoothing for optimal<br>post-processing analysis. Our results demonstrate that Bayesian<br>filtering techniques can significantly improve the signal-to-noise<br>ratio (SNR) of fMRI data while maintaining critical temporal<br>features necessary for RF control systems. Performance metrics<br>including filter latency, computational efficiency, and filtering<br>efficacy are analyzed across different noise conditions. The<br>proposed approach enables more robust RF control systems that<br>can adapt to the inherently noisy nature of fMRI signals.<br>Index Terms\u2014fMRI, Bayesian filtering, Kalman filter, Gaussian smoothing, RF control loops, real-time signal processing,<br>neuroimaging | bgilbert2<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/mastodon.social\/@Bgilbert1984\"><img loading=\"lazy\" decoding=\"async\" width=\"719\" height=\"717\" src=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/image-6.png\" alt=\"\" class=\"wp-image-4009\" srcset=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/image-6.png 719w, https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/image-6-300x300.png 300w, https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/10\/image-6-150x150.png 150w\" sizes=\"auto, (max-width: 719px) 100vw, 719px\" \/><\/a><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a novel approach for filteringfunctional Magnetic Resonance Imaging (fMRI) data streamsusing Bayesian techniques, specifically designed for real-timeRadio Frequency (RF) control loops. We implement and comparetwo primary filtering methods: causal Kalman filtering for realtime applications and non-causal Gaussian smoothing for optimalpost-processing analysis. Our results demonstrate that Bayesianfiltering techniques can significantly improve the signal-to-noiseratio&hellip;&nbsp;<\/p>\n","protected":false},"author":2,"featured_media":4009,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"class_list":["post-4007","page","type-page","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/4007","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4007"}],"version-history":[{"count":0,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/4007\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/media\/4009"}],"wp:attachment":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4007"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}