{"id":21108,"date":"2025-02-04T11:14:41","date_gmt":"2025-02-04T10:14:41","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&p=21108"},"modified":"2026-01-31T19:21:33","modified_gmt":"2026-01-31T18:21:33","slug":"explainable-graph-neural-networks-for-eeg-classification-and-seizure-detection-in-epileptic-patients","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/explainable-graph-neural-networks-for-eeg-classification-and-seizure-detection-in-epileptic-patients\/","title":{"rendered":"Explainable Graph Neural Networks for EEG Classification and Seizure Detection in Epileptic Patients"},"content":{"rendered":"\n

Szymon Mazurek, Rosmary Blanco, Joan Falc\u00f3-Roget, Alessandro Crimi<\/h2>\n\n\n\n
<\/div>\n\n\n\n

Electroencephalography (EEG) is currently the most used way to accurately diagnose epilepsy given its ability to measure hypersinchronized periods of brain activity known as seizures. However, EEG recordings are noisy and require trained practitioners for meaningful information to be extracted. Most importantly, further post hoc analyses are inherently time-consuming and subjective. Recent advances in artificial intelligence have paved the way to develop automated workflows easing the task of preprocessing and detecting epileptic activity from EEG. Yet, these models are ubiquitously difficult to interpret thus posing a challenge for its wide acceptance in clinical scenarios. Here, we propose a graph neural network enhanced with attention layers able to accurately and robustly identify pathological brain activity. We provide both feature and graph explanations for each prediction of the trained model. Crucially, we show how graph neural networks capture non-trivial dependencies between cortical regions that agree with the current clinical consensus. Altogether, these results highlight the fact that explainable artificial intelligence need not compromise its performance and represent an improvement in the applicability of artificial intelligence networks in clinical practice<\/p>\n\n\n\n

<\/div>\n\n\n\n

Authors<\/strong>: Szymon Mazurek, Rosmary Blanco, Joan Falc\u00f3-Roget, Alessandro Crimi<\/p>\n\n\n\n

DOI: <\/strong>10.1109\/ISBI56570.2024.10635821<\/a><\/p>\n\n\n\n

Keywords<\/strong>: EEG, epilepsy<\/p>\n\n\n\n

<\/div>\n\n\n\n\t\n \n \n\t\t\t\n\n\t\t\t\t\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n \n \n","protected":false},"excerpt":{"rendered":"

Conference manuscript in 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2024<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[136,15],"class_list":["post-21108","research","type-research","status-publish","hentry","research_type-publications","research_team-scientific-programmers","research_team-computational-neuroscience"],"yoast_head":"\nExplainable Graph Neural Networks for EEG Classification and Seizure Detection in Epileptic Patients - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/research\/explainable-graph-neural-networks-for-eeg-classification-and-seizure-detection-in-epileptic-patients\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Explainable Graph Neural Networks for EEG Classification and Seizure Detection in Epileptic Patients\" \/>\n<meta property=\"og:description\" content=\"Conference manuscript in 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2024\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/explainable-graph-neural-networks-for-eeg-classification-and-seizure-detection-in-epileptic-patients\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2026-01-31T18:21:33+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/explainable-graph-neural-networks-for-eeg-classification-and-seizure-detection-in-epileptic-patients\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/explainable-graph-neural-networks-for-eeg-classification-and-seizure-detection-in-epileptic-patients\\\/\",\"name\":\"Explainable Graph Neural Networks for EEG Classification and Seizure Detection in Epileptic Patients - 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However, EEG recordings are noisy and require trained practitioners for meaningful information to be extracted. Most importantly, further post hoc analyses are inherently time-consuming and subjective. Recent advances in artificial intelligence have paved the way to develop automated workflows easing the task of preprocessing and detecting epileptic activity from EEG. Yet, these models are ubiquitously difficult to interpret thus posing a challenge for its wide acceptance in clinical scenarios. Here, we propose a graph neural network enhanced with attention layers able to accurately and robustly identify pathological brain activity. We provide both feature and graph explanations for each prediction of the trained model. Crucially, we show how graph neural networks capture non-trivial dependencies between cortical regions that agree with the current clinical consensus. Altogether, these results highlight the fact that explainable artificial intelligence need not compromise its performance and represent an improvement in the applicability of artificial intelligence networks in clinical practice<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Electroencephalography (EEG) is currently the most used way to accurately diagnose epilepsy given its ability to measure hypersinchronized periods of brain activity known as seizures. However, EEG recordings are noisy and require trained practitioners for meaningful information to be extracted. Most importantly, further post hoc analyses are inherently time-consuming and subjective. Recent advances in artificial intelligence have paved the way to develop automated workflows easing the task of preprocessing and detecting epileptic activity from EEG. Yet, these models are ubiquitously difficult to interpret thus posing a challenge for its wide acceptance in clinical scenarios. Here, we propose a graph neural network enhanced with attention layers able to accurately and robustly identify pathological brain activity. We provide both feature and graph explanations for each prediction of the trained model. Crucially, we show how graph neural networks capture non-trivial dependencies between cortical regions that agree with the current clinical consensus. Altogether, these results highlight the fact that explainable artificial intelligence need not compromise its performance and represent an improvement in the applicability of artificial intelligence networks in clinical practice<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-moezvg","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-5FMDft","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: Szymon Mazurek, Rosmary Blanco, Joan Falc\u00f3-Roget, Alessandro Crimi<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: Szymon Mazurek, Rosmary Blanco, Joan Falc\u00f3-Roget, Alessandro Crimi<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-5FMDft","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>DOI: <\/strong><a href=\"https:\/\/doi.org\/10.1109\/ISBI56570.2024.10635821\" target=\"_blank\" rel=\"noreferrer noopener\">10.1109\/ISBI56570.2024.10635821<\/a><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>DOI: <\/strong><a href=\"https:\/\/doi.org\/10.1109\/ISBI56570.2024.10635821\" target=\"_blank\" rel=\"noreferrer noopener\">10.1109\/ISBI56570.2024.10635821<\/a><\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-bK0bPv","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: EEG, epilepsy<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: EEG, epilepsy<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-moezvg","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"READ HERE","button_type":"link","url":"https:\/\/ieeexplore.ieee.org\/abstract\/document\/10635821","button_style":"primary","target":"_self","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/21108","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":9,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/21108\/revisions"}],"predecessor-version":[{"id":21126,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/21108\/revisions\/21126"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=21108"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=21108"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=21108"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}