Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks
Abstract Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI). Traditional methods often fall short in capturing the intricate temporal dynamics inherent in EEG data. This paper explores the...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-10270-0 |
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| author | Aaditya Joshi Paramveer Singh Matharu Lokesh Malviya Manoj Kumar Akshay Jadhav |
| author_facet | Aaditya Joshi Paramveer Singh Matharu Lokesh Malviya Manoj Kumar Akshay Jadhav |
| author_sort | Aaditya Joshi |
| collection | DOAJ |
| description | Abstract Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI). Traditional methods often fall short in capturing the intricate temporal dynamics inherent in EEG data. This paper explores the use of Convolutional Spiking Neural Networks (CSNNs) to enhance EEG signal classification. We apply Discrete Wavelet Transform (DWT) for feature extraction and evaluate CSNN performance on the Physionet EEG dataset, benchmarking it against traditional deep learning and machine learning methods. The findings indicate that CSNNs achieve high accuracy, reaching 98.75% in 10-fold cross-validation, and an impressive F1 score of 98.60%. Notably, this F1-score represents an improvement over previous benchmarks, highlighting the effectiveness of our approach. Along with offering advantages in temporal precision and energy efficiency, CSNNs emerge as a promising solution for next-generation EEG analysis systems. |
| format | Article |
| id | doaj-art-bae05bb8fc2e4cdc85ad4b7c8e54cee3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bae05bb8fc2e4cdc85ad4b7c8e54cee32025-08-20T03:46:01ZengNature PortfolioScientific Reports2045-23222025-07-0115111410.1038/s41598-025-10270-0Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networksAaditya Joshi0Paramveer Singh Matharu1Lokesh Malviya2Manoj Kumar3Akshay Jadhav4VIT Bhopal UniversityVIT Bhopal UniversityVIT Bhopal UniversityVIT Bhopal UniversityManipal University JaipurAbstract Accurate and efficient analysis of Electroencephalogram (EEG) signals is crucial for applications like neurological diagnosis and Brain-Computer Interfaces (BCI). Traditional methods often fall short in capturing the intricate temporal dynamics inherent in EEG data. This paper explores the use of Convolutional Spiking Neural Networks (CSNNs) to enhance EEG signal classification. We apply Discrete Wavelet Transform (DWT) for feature extraction and evaluate CSNN performance on the Physionet EEG dataset, benchmarking it against traditional deep learning and machine learning methods. The findings indicate that CSNNs achieve high accuracy, reaching 98.75% in 10-fold cross-validation, and an impressive F1 score of 98.60%. Notably, this F1-score represents an improvement over previous benchmarks, highlighting the effectiveness of our approach. Along with offering advantages in temporal precision and energy efficiency, CSNNs emerge as a promising solution for next-generation EEG analysis systems.https://doi.org/10.1038/s41598-025-10270-0Electroencephalogram (EEG)Spiking neural networks (SNN)Convolutional spiking neural networks (CSNN) |
| spellingShingle | Aaditya Joshi Paramveer Singh Matharu Lokesh Malviya Manoj Kumar Akshay Jadhav Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks Scientific Reports Electroencephalogram (EEG) Spiking neural networks (SNN) Convolutional spiking neural networks (CSNN) |
| title | Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks |
| title_full | Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks |
| title_fullStr | Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks |
| title_full_unstemmed | Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks |
| title_short | Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks |
| title_sort | advancing eeg based stress detection using spiking neural networks and convolutional spiking neural networks |
| topic | Electroencephalogram (EEG) Spiking neural networks (SNN) Convolutional spiking neural networks (CSNN) |
| url | https://doi.org/10.1038/s41598-025-10270-0 |
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