Exploring Contrastive Learning Neural-Congruency on EEG Recording of Children with Dyslexia
Electroencephalogram (EEG) recordings of children are often used to study the underlying neural basis of causal factors of reading disorders and dyslexia. However, the inter-subject variability in EEG and the unconstrained nature of reading experiments used to elicit these factors made it challengin...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2024-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/135385 |
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| author | Christoforos Christoforou Jacqueline Torres M. Timothy Papadopoulos C. Maria Theodorou |
| author_facet | Christoforos Christoforou Jacqueline Torres M. Timothy Papadopoulos C. Maria Theodorou |
| author_sort | Christoforos Christoforou |
| collection | DOAJ |
| description | Electroencephalogram (EEG) recordings of children are often used to study the underlying neural basis of causal factors of reading disorders and dyslexia. However, the inter-subject variability in EEG and the unconstrained nature of reading experiments used to elicit these factors made it challenging for traditional EEG analysis methods to extract neural components of these factors. In this work, we aim to explore the use of novel deep neural network architectures and contrastive learning methods to overcome the methodological limitations of traditional techniques and enhance the extraction process of neural components during reading tasks. Notably, we formulate a neural network architecture to extract EEG embedding using contrastive loss that maximizes the neural congruency in non-dyslexic children compared to children with dyslexia. We plan to evaluate our approach on three EEG datasets involving children with dyslexia performing Rapid Automatized Naming (RAN) and Phonological Processing (PA) tasks. The proposed contrastive learning framework will provide an enhanced tool to facilitate studying the neural underpinnings of naming speed and their association with reading performance and related difficulties. |
| format | Article |
| id | doaj-art-c3099cd72e9b417ab6bd28a9848f09d9 |
| institution | OA Journals |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-c3099cd72e9b417ab6bd28a9848f09d92025-08-20T01:52:19ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622024-05-013710.32473/flairs.37.1.13538571758Exploring Contrastive Learning Neural-Congruency on EEG Recording of Children with DyslexiaChristoforos Christoforou0https://orcid.org/0000-0003-1864-5862Jacqueline Torres M.Timothy Papadopoulos C.Maria TheodorouSt. John's UniversityElectroencephalogram (EEG) recordings of children are often used to study the underlying neural basis of causal factors of reading disorders and dyslexia. However, the inter-subject variability in EEG and the unconstrained nature of reading experiments used to elicit these factors made it challenging for traditional EEG analysis methods to extract neural components of these factors. In this work, we aim to explore the use of novel deep neural network architectures and contrastive learning methods to overcome the methodological limitations of traditional techniques and enhance the extraction process of neural components during reading tasks. Notably, we formulate a neural network architecture to extract EEG embedding using contrastive loss that maximizes the neural congruency in non-dyslexic children compared to children with dyslexia. We plan to evaluate our approach on three EEG datasets involving children with dyslexia performing Rapid Automatized Naming (RAN) and Phonological Processing (PA) tasks. The proposed contrastive learning framework will provide an enhanced tool to facilitate studying the neural underpinnings of naming speed and their association with reading performance and related difficulties.https://journals.flvc.org/FLAIRS/article/view/135385 |
| spellingShingle | Christoforos Christoforou Jacqueline Torres M. Timothy Papadopoulos C. Maria Theodorou Exploring Contrastive Learning Neural-Congruency on EEG Recording of Children with Dyslexia Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| title | Exploring Contrastive Learning Neural-Congruency on EEG Recording of Children with Dyslexia |
| title_full | Exploring Contrastive Learning Neural-Congruency on EEG Recording of Children with Dyslexia |
| title_fullStr | Exploring Contrastive Learning Neural-Congruency on EEG Recording of Children with Dyslexia |
| title_full_unstemmed | Exploring Contrastive Learning Neural-Congruency on EEG Recording of Children with Dyslexia |
| title_short | Exploring Contrastive Learning Neural-Congruency on EEG Recording of Children with Dyslexia |
| title_sort | exploring contrastive learning neural congruency on eeg recording of children with dyslexia |
| url | https://journals.flvc.org/FLAIRS/article/view/135385 |
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