{"id":4176,"date":"2025-10-24T15:18:01","date_gmt":"2025-10-24T15:18:01","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4176"},"modified":"2025-10-25T01:01:52","modified_gmt":"2025-10-25T01:01:52","slug":"csi%e2%86%92voxel-wi-fi-sensing-as-a-low-cost-fmri-proxy","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4176","title":{"rendered":"CSI\u2192Voxel: Wi-Fi Sensing as a Low-Cost fMRI Proxy"},"content":{"rendered":"\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-spectrcyde wp-block-embed-spectrcyde\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"mHgQGf32bS\"><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4172\">CSI\u2192Voxel: Wi-Fi Sensing as a Low-Cost fMRI Proxy<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;CSI\u2192Voxel: Wi-Fi Sensing as a Low-Cost fMRI Proxy&#8221; &#8212; Spectrcyde\" src=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=4172&#038;embed=true#?secret=FDfKATwEk5#?secret=mHgQGf32bS\" data-secret=\"mHgQGf32bS\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>Functional magnetic resonance imaging (fMRI) provides<br>high-resolution, voxel-wise measurements of brain activity, but<br>acquiring large-scale fMRI datasets is expensive, immobile,<br>and time-consuming. At the same time, commodity wireless<br>devices continually capture channel state information (CSI)<br>\u2014 a rich, multi-dimensional signal that reflects environmental<br>changes and human motion. This paper investigates whether<br>carefully processed CSI can serve as a low-cost, portable<br>proxy for coarse voxel-wise neural activation in controlled<br>experimental paradigms.<br>Our goal is not to replace fMRI, but to explore whether CSI<br>contains enough information to reconstruct low-dimensional<br>summaries of neural activity and to detect block-like activation<br>patterns under controlled conditions. If successful, such a<br>proxy could enable inexpensive, mobile monitoring and rapid<br>prototyping of neuroimaging-inspired interfaces.<br>Challenges. Mapping CSI to voxel-like signals presents<br>several technical challenges: (i) differing sampling rates and<br>clocks (Wi-Fi CSI is sampled at high frequency while fMRI<br>TRs are much slower), (ii) unknown and time-varying delays<br>(clock offsets and drift) between sensors, (iii) heterogeneity<br>across subcarriers and receiving antennas, and (iv) severe,<br>structured noise due to multipath and non-neural motion.<br>Addressing these requires robust preprocessing, alignment,<br>and decoder design that tolerate misalignment and domain<br>mismatch.<br>Approach. We present a simulation-led pipeline that synthesizes paired CSI and voxel-like time series under controlled<br>offsets and drift, applies alignment and time-warping to synchronize signals, and trains simple linear decoders to predict<br>voxel activity from aggregated CSI features. The pipeline<br>produces three primary figures: (1) alignment timelines (before\/after), (2) per-voxel correlation distributions, and (3) ROC<br>curves for block-activity detection. All scripts and synthetic<br>data are provided so results are reproducible and the pipeline<br>can be reused as a test harness.<br>Contributions. This work makes four concrete contributions:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A compact, reproducible simulation and processing<br>pipeline that generates paired CSI and voxel signals with<br>configurable offsets and drift.<\/li>\n\n\n\n<li>A lightweight alignment method (lag estimation + linear<br>time-warp) that corrects clock offsets and drift between<br>modalities.<\/li>\n\n\n\n<li>An empirical evaluation showing that aggregated CSI<br>features, coupled with simple ridge decoders, recover<br>coarse voxel activity and detect block activations with<br>non-trivial AUC in simulation.<\/li>\n\n\n\n<li>A small, self-contained LaTeX project (scripts, figures,<br>and captions) that demonstrates the pipeline and provides<br>a press-style build target for rapid iteration.<br>Outline. The remainder of the paper is organized as follows.<br>Section II describes the synthetic data generation, feature extraction, and alignment procedures. Section III details the experimental settings and evaluation metrics. Section ?? presents<br>the alignment, correlation, and ROC figures, and Section ??<br>discusses limitations and next steps toward real-world CSI-tovoxel evaluation.<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p>Updates In Progress:<\/p>\n\n\n\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:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/10\/Wi-Fi-Sensing-as-a-Low-Cost-fMRI-Proxy-CSI-Voxel-Engine-bgilbert1984.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of Wi-Fi Sensing as a Low-Cost fMRI Proxy CSI Voxel Engine bgilbert1984.\"><\/object><a id=\"wp-block-file--media-fd55b328-4628-43b2-a22b-f9447fc00a5b\" href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/10\/Wi-Fi-Sensing-as-a-Low-Cost-fMRI-Proxy-CSI-Voxel-Engine-bgilbert1984.pdf\">Wi-Fi Sensing as a Low-Cost fMRI Proxy CSI Voxel Engine bgilbert1984<\/a><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/10\/Wi-Fi-Sensing-as-a-Low-Cost-fMRI-Proxy-CSI-Voxel-Engine-bgilbert1984.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-fd55b328-4628-43b2-a22b-f9447fc00a5b\">Download<\/a><\/div>\n\n\n\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:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/10\/CSI2Voxel_pipeline-rev-2.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of CSI2Voxel_pipeline rev 2.\"><\/object><a id=\"wp-block-file--media-a1e09bc0-7a8f-4aa5-9bb7-8e2cbad5b0e8\" href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/10\/CSI2Voxel_pipeline-rev-2.pdf\">CSI2Voxel_pipeline rev 2<\/a><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/10\/CSI2Voxel_pipeline-rev-2.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-a1e09bc0-7a8f-4aa5-9bb7-8e2cbad5b0e8\">Download<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Functional magnetic resonance imaging (fMRI) provideshigh-resolution, voxel-wise measurements of brain activity, butacquiring large-scale fMRI datasets is expensive, immobile,and time-consuming. At the same time, commodity wirelessdevices continually capture channel state information (CSI)\u2014 a rich, multi-dimensional signal that reflects environmentalchanges and human motion. This paper investigates whethercarefully processed CSI can serve as a low-cost, portableproxy for coarse&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4176\" rel=\"bookmark\"><span class=\"screen-reader-text\">CSI\u2192Voxel: Wi-Fi Sensing as a Low-Cost fMRI Proxy<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":3308,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","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":""},"categories":[10],"tags":[],"class_list":["post-4176","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4176","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4176"}],"version-history":[{"count":3,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4176\/revisions"}],"predecessor-version":[{"id":4182,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4176\/revisions\/4182"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/3308"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4176"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4176"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4176"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}