Neural decoding of Aristotle tactile illusion using deep learning-based fMRI classification
IntroductionAristotle illusion is a well-known tactile illusion which causes the perception of one object as two. EEG analysis was employed to investigate the neural correlates of Aristotle illusion, yet was limited due to low spatial resolution of EEG. This study aimed to identify brain regions inv...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Neuroscience |
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| author | Eunji Lee Eunji Lee Ji-Hyun Kim Jaeseok Park Jaeseok Park Sung-Phil Kim Taehoon Shin Taehoon Shin Taehoon Shin |
| author_facet | Eunji Lee Eunji Lee Ji-Hyun Kim Jaeseok Park Jaeseok Park Sung-Phil Kim Taehoon Shin Taehoon Shin Taehoon Shin |
| author_sort | Eunji Lee |
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| description | IntroductionAristotle illusion is a well-known tactile illusion which causes the perception of one object as two. EEG analysis was employed to investigate the neural correlates of Aristotle illusion, yet was limited due to low spatial resolution of EEG. This study aimed to identify brain regions involved in the Aristotle illusion using functional magnetic resonance imaging (fMRI) and deep learning-based analysis of fMRI data.MethodsWhile three types of tactile stimuli (Aristotle, Reverse, Asynchronous) were applied to thirty participants’ fingers, we collected fMRI data, and recorded the number of stimuli each participant perceived. Four convolutional neural network (CNN) models were trained for perception-based classification tasks (the occurrence of Aristotle illusion vs. Reverse illusion, the occurrence vs. absence of Reverse illusion), and stimulus-based classification tasks (Aristotle vs. Reverse, Reverse vs. Asynchronous, and Aristotle vs. Asynchronous).ResultsSimple fully convolution network (SFCN) achieved the highest classification accuracy of 68.4% for the occurrence of Aristotle illusion vs. Reverse illusion, and 80.1% for the occurrence vs. absence of Reverse illusion. For stimulus-based classification tasks, all CNN models yielded accuracies around 50% failing to distinguish among the three types of applied stimuli. Gradient-weighted class activation mapping (Grad-CAM) analysis revealed salient brain regions-of-interest (ROIs) for the perception-based classification tasks, including the somatosensory cortex and parietal regions.DiscussionOur findings demonstrate that perception-driven neural responses are classifiable using fMRI-based CNN models. Saliency analysis of the trained CNNs reveals the involvement of the somatosensory cortex and parietal regions in making classification decisions, consistent with previous research. Other salient ROIs include orbitofrontal cortex, middle temporal pole, supplementary motor area, and middle cingulate cortex. |
| format | Article |
| id | doaj-art-b2cc5ee365014048b4de82f83f93ca38 |
| institution | OA Journals |
| issn | 1662-453X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-b2cc5ee365014048b4de82f83f93ca382025-08-20T02:09:51ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2025-06-011910.3389/fnins.2025.16068011606801Neural decoding of Aristotle tactile illusion using deep learning-based fMRI classificationEunji Lee0Eunji Lee1Ji-Hyun Kim2Jaeseok Park3Jaeseok Park4Sung-Phil Kim5Taehoon Shin6Taehoon Shin7Taehoon Shin8Department of Mechanical and Biomedical Engineering, Ewha W. University, Seoul, Republic of KoreaGraduate Program in Smart Factory, Ewha W. University, Seoul, Republic of KoreaDepartment of Cognitive and Psychological Sciences, Brown University, Providence, RI, United StatesDepartment of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of KoreaDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of KoreaDepartment of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of KoreaDepartment of Mechanical and Biomedical Engineering, Ewha W. University, Seoul, Republic of KoreaGraduate Program in Smart Factory, Ewha W. University, Seoul, Republic of KoreaDepartment of Artificial Intelligence and Software, Ewha W. University, Seoul, Republic of KoreaIntroductionAristotle illusion is a well-known tactile illusion which causes the perception of one object as two. EEG analysis was employed to investigate the neural correlates of Aristotle illusion, yet was limited due to low spatial resolution of EEG. This study aimed to identify brain regions involved in the Aristotle illusion using functional magnetic resonance imaging (fMRI) and deep learning-based analysis of fMRI data.MethodsWhile three types of tactile stimuli (Aristotle, Reverse, Asynchronous) were applied to thirty participants’ fingers, we collected fMRI data, and recorded the number of stimuli each participant perceived. Four convolutional neural network (CNN) models were trained for perception-based classification tasks (the occurrence of Aristotle illusion vs. Reverse illusion, the occurrence vs. absence of Reverse illusion), and stimulus-based classification tasks (Aristotle vs. Reverse, Reverse vs. Asynchronous, and Aristotle vs. Asynchronous).ResultsSimple fully convolution network (SFCN) achieved the highest classification accuracy of 68.4% for the occurrence of Aristotle illusion vs. Reverse illusion, and 80.1% for the occurrence vs. absence of Reverse illusion. For stimulus-based classification tasks, all CNN models yielded accuracies around 50% failing to distinguish among the three types of applied stimuli. Gradient-weighted class activation mapping (Grad-CAM) analysis revealed salient brain regions-of-interest (ROIs) for the perception-based classification tasks, including the somatosensory cortex and parietal regions.DiscussionOur findings demonstrate that perception-driven neural responses are classifiable using fMRI-based CNN models. Saliency analysis of the trained CNNs reveals the involvement of the somatosensory cortex and parietal regions in making classification decisions, consistent with previous research. Other salient ROIs include orbitofrontal cortex, middle temporal pole, supplementary motor area, and middle cingulate cortex.https://www.frontiersin.org/articles/10.3389/fnins.2025.1606801/fullsomatosensorytactile illusionfMRIdeep learningbrain mapping |
| spellingShingle | Eunji Lee Eunji Lee Ji-Hyun Kim Jaeseok Park Jaeseok Park Sung-Phil Kim Taehoon Shin Taehoon Shin Taehoon Shin Neural decoding of Aristotle tactile illusion using deep learning-based fMRI classification Frontiers in Neuroscience somatosensory tactile illusion fMRI deep learning brain mapping |
| title | Neural decoding of Aristotle tactile illusion using deep learning-based fMRI classification |
| title_full | Neural decoding of Aristotle tactile illusion using deep learning-based fMRI classification |
| title_fullStr | Neural decoding of Aristotle tactile illusion using deep learning-based fMRI classification |
| title_full_unstemmed | Neural decoding of Aristotle tactile illusion using deep learning-based fMRI classification |
| title_short | Neural decoding of Aristotle tactile illusion using deep learning-based fMRI classification |
| title_sort | neural decoding of aristotle tactile illusion using deep learning based fmri classification |
| topic | somatosensory tactile illusion fMRI deep learning brain mapping |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2025.1606801/full |
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