Modeling of whole brain sleep electroencephalogram using deep oscillatory neural network
This study presents a general trainable network of Hopf oscillators to model high-dimensional electroencephalogram (EEG) signals across different sleep stages. The proposed architecture consists of two main components: a layer of interconnected oscillators and a complex-valued feed-forward network d...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2025.1513374/full |
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| author | Sayan Ghosh Dipayan Biswas N. R. Rohan Sujith Vijayan V. Srinivasa Chakravarthy |
| author_facet | Sayan Ghosh Dipayan Biswas N. R. Rohan Sujith Vijayan V. Srinivasa Chakravarthy |
| author_sort | Sayan Ghosh |
| collection | DOAJ |
| description | This study presents a general trainable network of Hopf oscillators to model high-dimensional electroencephalogram (EEG) signals across different sleep stages. The proposed architecture consists of two main components: a layer of interconnected oscillators and a complex-valued feed-forward network designed with and without a hidden layer. Incorporating a hidden layer in the feed-forward network leads to lower reconstruction errors than the simpler version without it. Our model reconstructs EEG signals across all five sleep stages and predicts the subsequent 5 s of EEG activity. The predicted data closely aligns with the empirical EEG regarding mean absolute error, power spectral similarity, and complexity measures. We propose three models, each representing a stage of increasing complexity from initial training to architectures with and without hidden layers. In these models, the oscillators initially lack spatial localization. However, we introduce spatial constraints in the final two models by superimposing spherical shells and rectangular geometries onto the oscillator network. Overall, the proposed model represents a step toward constructing a large-scale, biologically inspired model of brain dynamics. |
| format | Article |
| id | doaj-art-3764260c5a0f485ea64effce5f07e395 |
| institution | DOAJ |
| issn | 1662-5196 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj-art-3764260c5a0f485ea64effce5f07e3952025-08-20T03:09:48ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962025-05-011910.3389/fninf.2025.15133741513374Modeling of whole brain sleep electroencephalogram using deep oscillatory neural networkSayan Ghosh0Dipayan Biswas1N. R. Rohan2Sujith Vijayan3V. Srinivasa Chakravarthy4Indian Institute of Technology Madras, Chennai, IndiaIndian Institute of Technology Madras, Chennai, IndiaIndian Institute of Technology Madras, Chennai, IndiaVirginia Tech, Blacksburg, VA, United StatesIndian Institute of Technology Madras, Chennai, IndiaThis study presents a general trainable network of Hopf oscillators to model high-dimensional electroencephalogram (EEG) signals across different sleep stages. The proposed architecture consists of two main components: a layer of interconnected oscillators and a complex-valued feed-forward network designed with and without a hidden layer. Incorporating a hidden layer in the feed-forward network leads to lower reconstruction errors than the simpler version without it. Our model reconstructs EEG signals across all five sleep stages and predicts the subsequent 5 s of EEG activity. The predicted data closely aligns with the empirical EEG regarding mean absolute error, power spectral similarity, and complexity measures. We propose three models, each representing a stage of increasing complexity from initial training to architectures with and without hidden layers. In these models, the oscillators initially lack spatial localization. However, we introduce spatial constraints in the final two models by superimposing spherical shells and rectangular geometries onto the oscillator network. Overall, the proposed model represents a step toward constructing a large-scale, biologically inspired model of brain dynamics.https://www.frontiersin.org/articles/10.3389/fninf.2025.1513374/fullEEGHopf oscillatorsleep stages modelinglarge scale brain dynamicsbiomedical signal analysisHopf oscillator model |
| spellingShingle | Sayan Ghosh Dipayan Biswas N. R. Rohan Sujith Vijayan V. Srinivasa Chakravarthy Modeling of whole brain sleep electroencephalogram using deep oscillatory neural network Frontiers in Neuroinformatics EEG Hopf oscillator sleep stages modeling large scale brain dynamics biomedical signal analysis Hopf oscillator model |
| title | Modeling of whole brain sleep electroencephalogram using deep oscillatory neural network |
| title_full | Modeling of whole brain sleep electroencephalogram using deep oscillatory neural network |
| title_fullStr | Modeling of whole brain sleep electroencephalogram using deep oscillatory neural network |
| title_full_unstemmed | Modeling of whole brain sleep electroencephalogram using deep oscillatory neural network |
| title_short | Modeling of whole brain sleep electroencephalogram using deep oscillatory neural network |
| title_sort | modeling of whole brain sleep electroencephalogram using deep oscillatory neural network |
| topic | EEG Hopf oscillator sleep stages modeling large scale brain dynamics biomedical signal analysis Hopf oscillator model |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2025.1513374/full |
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