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In this work, we propose a biologically inspired filter significance assessment method based on Steady-State Visually Evoked Potentials (SSVEPs), a well-established neuroscience principle. Our approach leverages frequency tagging techniques to quantify the importance of convolutional filters by analyzing their frequency-locked responses to periodic contrast modulations in input images. By blending SSVEP-based filter selection into Class Activation Mapping (CAM) frameworks such as Grad-CAM, Grad-CAM++, EigenCAM, and LayerCAM, we enhance model interpretability while reducing attribution noise. Experimental evaluations on ImageNet using VGG-16, ResNet-50, and ResNeXt-50 demonstrate that SSVEP-enhanced CAM methods improve spatial focus in visual explanations, yielding higher energy concentration while maintaining competitive localization accuracy. These findings suggest that our biologically inspired approach offers a robust mechanism for identifying key filters in CNNs, paving the way for more interpretable and transparent deep learning models.<\/jats:p>","DOI":"10.1007\/978-3-032-08324-1_19","type":"book-chapter","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T08:49:07Z","timestamp":1760518147000},"page":"422-435","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Biologically Inspired Filter Significance Assessment Method for\u00a0Model Explanation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5405-8557","authenticated-orcid":false,"given":"Emirhan","family":"B\u00f6ge","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8315-2461","authenticated-orcid":false,"given":"Yasemin","family":"Gunindi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2651-7301","authenticated-orcid":false,"given":"Murat Bilgehan","family":"Ertan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6168-2883","authenticated-orcid":false,"given":"Erchan","family":"Aptoula","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9854-2833","authenticated-orcid":false,"given":"Nihan","family":"Alp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5539-9085","authenticated-orcid":false,"given":"Huseyin","family":"Ozkan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"19_CR1","unstructured":"Akhtar, N.: A survey of explainable AI in deep visual modeling: Methods and metrics. arXiv preprint arXiv:2301.13445 (2023)"},{"issue":"1","key":"19_CR2","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1038\/s41598-021-02808-9","volume":"12","author":"N Alp","year":"2022","unstructured":"Alp, N., Ozkan, H.: Neural correlates of integration processes during dynamic face perception. 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