StressNet: Hybrid model of LSTM and CNN for stress detection from electroencephalogram signal (EEG)
Everyday tasks can cause stress, which can lead to serious medical conditions, such as depression. EEG signal processing can assist medical professionals in managing the emotional state of patients. To detect stress states in EEG signals, we propose a new architecture, StressNet, which is a combinat...
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| Vydané v: | Results in control and optimization Ročník 11; s. 100231 |
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Elsevier B.V
01.06.2023
Elsevier |
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| ISSN: | 2666-7207, 2666-7207 |
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| Abstract | Everyday tasks can cause stress, which can lead to serious medical conditions, such as depression. EEG signal processing can assist medical professionals in managing the emotional state of patients. To detect stress states in EEG signals, we propose a new architecture, StressNet, which is a combination of a two-dimensional convolutional neural network (CNN) and a long short-term memory (LSTM) network. The EEG signals are first decomposed into alpha, beta, and theta signals, which are then used to generate azimuthal projection-based images. These images are fed into the 2D CNN for feature extraction, which are then passed to the LSTM for further processing. The LSTM’s ability to remember past states makes it particularly effective in modeling the temporal dynamics of EEG signals. Finally, the StressNet model classifies the features using fully connected layers into either stress or normal classes. We evaluate the performance of our model on the DEEP and SEED datasets and compare it to other methods. The results show that the proposed StressNet model outperforms the human stress detection accuracy, achieving an accuracy of 97.8%. |
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| AbstractList | Everyday tasks can cause stress, which can lead to serious medical conditions, such as depression. EEG signal processing can assist medical professionals in managing the emotional state of patients. To detect stress states in EEG signals, we propose a new architecture, StressNet, which is a combination of a two-dimensional convolutional neural network (CNN) and a long short-term memory (LSTM) network. The EEG signals are first decomposed into alpha, beta, and theta signals, which are then used to generate azimuthal projection-based images. These images are fed into the 2D CNN for feature extraction, which are then passed to the LSTM for further processing. The LSTM’s ability to remember past states makes it particularly effective in modeling the temporal dynamics of EEG signals. Finally, the StressNet model classifies the features using fully connected layers into either stress or normal classes. We evaluate the performance of our model on the DEEP and SEED datasets and compare it to other methods. The results show that the proposed StressNet model outperforms the human stress detection accuracy, achieving an accuracy of 97.8%. |
| ArticleNumber | 100231 |
| Author | Shinde, Arundhati Mane, Swaymprabha Alias Megha |
| Author_xml | – sequence: 1 givenname: Swaymprabha Alias Megha orcidid: 0000-0002-6022-9144 surname: Mane fullname: Mane, Swaymprabha Alias Megha email: er.meghampatil@gmail.com – sequence: 2 givenname: Arundhati surname: Shinde fullname: Shinde, Arundhati email: aashinde@bvucoep.edu.in |
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| Cites_doi | 10.1007/s13198-021-01468-2 10.1088/1741-2560/14/1/016003 10.1109/TNSRE.2019.2938295 10.3390/s19061423 10.1186/s12984-017-0261-y 10.3390/s19071736 10.1016/j.media.2017.07.005 10.1155/2022/9579422 10.1016/j.neunet.2018.09.009 10.1007/978-981-15-5558-9_68 10.1016/j.bbe.2020.05.008 10.1109/ACCESS.2019.2934018 10.1109/TNNLS.2018.2789927 10.3390/s21248370 10.1109/ACCESS.2019.2930958 10.1145/3020078.3021740 10.3389/fneur.2019.00806 10.3390/e21121199 10.1109/TNSRE.2016.2601240 10.3390/mi13081208 10.3389/fnbot.2021.819448 |
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| Keywords | Deep learning Human stress detection Accuracy Electroencephalography (EEG) Brain–computer interfaces (BCIs) Convolutional neural network (CNN) Long short-term memory (LSTM) Stress Azimuthal projection |
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