Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals
Abstract Mental stress is an issue that creates functional limitations in the workplace. Chronic stress leads to a number of psychophysiological sicknesses. For instance, it raises the risk of depression, heart attack, and stroke. According to the most recent findings in neuroscience, the human brai...
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| Autor*in: |
Rahman, Atta Ur [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 26(2022), 20 vom: 23. Feb., Seite 10687-10698 |
|---|---|
| Übergeordnetes Werk: |
volume:26 ; year:2022 ; number:20 ; day:23 ; month:02 ; pages:10687-10698 |
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| DOI / URN: |
10.1007/s00500-022-06847-w |
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| 520 | |a Abstract Mental stress is an issue that creates functional limitations in the workplace. Chronic stress leads to a number of psychophysiological sicknesses. For instance, it raises the risk of depression, heart attack, and stroke. According to the most recent findings in neuroscience, the human brain is the primary focus of mental stress. Perception of biological motion in the human brain determines the risky and stressful situations. Neural signaling of the human brain is used as an objective measure for determining the stress level of a subject. The oscillations of electroencephalography (EEG) signals are utilized for classifying human stress. EEG signals have a higher temporal resolution and are rapidly distorted with unwanted noise, resulting in a variety of artifacts. This study utilizes Extended Independent Component Analysis based approach for artifacts removal. A Multiclass Common Spatial Pattern-based moving window technique is proposed here to obtain the most distinguishable time segment of EEG trials. BiLSTM is used to improve classification results. In order to validate the model performance, two publically available datasets (i.e., DEAP and SEED) are utilized. Employing these datasets, the proposed model achieves state-of-the-art results (93.1, 96.84%) for EEG signal classification to identify stress. | ||
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10.1007/s00500-022-06847-w doi (DE-627)SPR048171964 (SPR)s00500-022-06847-w-e DE-627 ger DE-627 rakwb eng Rahman, Atta Ur verfasserin aut Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Mental stress is an issue that creates functional limitations in the workplace. Chronic stress leads to a number of psychophysiological sicknesses. For instance, it raises the risk of depression, heart attack, and stroke. According to the most recent findings in neuroscience, the human brain is the primary focus of mental stress. Perception of biological motion in the human brain determines the risky and stressful situations. Neural signaling of the human brain is used as an objective measure for determining the stress level of a subject. The oscillations of electroencephalography (EEG) signals are utilized for classifying human stress. EEG signals have a higher temporal resolution and are rapidly distorted with unwanted noise, resulting in a variety of artifacts. This study utilizes Extended Independent Component Analysis based approach for artifacts removal. A Multiclass Common Spatial Pattern-based moving window technique is proposed here to obtain the most distinguishable time segment of EEG trials. BiLSTM is used to improve classification results. In order to validate the model performance, two publically available datasets (i.e., DEAP and SEED) are utilized. Employing these datasets, the proposed model achieves state-of-the-art results (93.1, 96.84%) for EEG signal classification to identify stress. EEG signals (dpeaa)DE-He213 Stress detection (dpeaa)DE-He213 Artifacts removal (dpeaa)DE-He213 Common spatial pattern (dpeaa)DE-He213 BiLSTM (dpeaa)DE-He213 Tubaishat, Abdallah aut Al-Obeidat, Feras aut Halim, Zahid (orcid)0000-0003-3094-3483 aut Tahir, Madiha aut Qayum, Fawad aut Enthalten in Soft Computing Springer-Verlag, 2003 26(2022), 20 vom: 23. Feb., Seite 10687-10698 (DE-627)SPR006469531 nnns volume:26 year:2022 number:20 day:23 month:02 pages:10687-10698 https://dx.doi.org/10.1007/s00500-022-06847-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2022 20 23 02 10687-10698 |
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10.1007/s00500-022-06847-w doi (DE-627)SPR048171964 (SPR)s00500-022-06847-w-e DE-627 ger DE-627 rakwb eng Rahman, Atta Ur verfasserin aut Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Mental stress is an issue that creates functional limitations in the workplace. Chronic stress leads to a number of psychophysiological sicknesses. For instance, it raises the risk of depression, heart attack, and stroke. According to the most recent findings in neuroscience, the human brain is the primary focus of mental stress. Perception of biological motion in the human brain determines the risky and stressful situations. Neural signaling of the human brain is used as an objective measure for determining the stress level of a subject. The oscillations of electroencephalography (EEG) signals are utilized for classifying human stress. EEG signals have a higher temporal resolution and are rapidly distorted with unwanted noise, resulting in a variety of artifacts. This study utilizes Extended Independent Component Analysis based approach for artifacts removal. A Multiclass Common Spatial Pattern-based moving window technique is proposed here to obtain the most distinguishable time segment of EEG trials. BiLSTM is used to improve classification results. In order to validate the model performance, two publically available datasets (i.e., DEAP and SEED) are utilized. Employing these datasets, the proposed model achieves state-of-the-art results (93.1, 96.84%) for EEG signal classification to identify stress. EEG signals (dpeaa)DE-He213 Stress detection (dpeaa)DE-He213 Artifacts removal (dpeaa)DE-He213 Common spatial pattern (dpeaa)DE-He213 BiLSTM (dpeaa)DE-He213 Tubaishat, Abdallah aut Al-Obeidat, Feras aut Halim, Zahid (orcid)0000-0003-3094-3483 aut Tahir, Madiha aut Qayum, Fawad aut Enthalten in Soft Computing Springer-Verlag, 2003 26(2022), 20 vom: 23. Feb., Seite 10687-10698 (DE-627)SPR006469531 nnns volume:26 year:2022 number:20 day:23 month:02 pages:10687-10698 https://dx.doi.org/10.1007/s00500-022-06847-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2022 20 23 02 10687-10698 |
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10.1007/s00500-022-06847-w doi (DE-627)SPR048171964 (SPR)s00500-022-06847-w-e DE-627 ger DE-627 rakwb eng Rahman, Atta Ur verfasserin aut Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Mental stress is an issue that creates functional limitations in the workplace. Chronic stress leads to a number of psychophysiological sicknesses. For instance, it raises the risk of depression, heart attack, and stroke. According to the most recent findings in neuroscience, the human brain is the primary focus of mental stress. Perception of biological motion in the human brain determines the risky and stressful situations. Neural signaling of the human brain is used as an objective measure for determining the stress level of a subject. The oscillations of electroencephalography (EEG) signals are utilized for classifying human stress. EEG signals have a higher temporal resolution and are rapidly distorted with unwanted noise, resulting in a variety of artifacts. This study utilizes Extended Independent Component Analysis based approach for artifacts removal. A Multiclass Common Spatial Pattern-based moving window technique is proposed here to obtain the most distinguishable time segment of EEG trials. BiLSTM is used to improve classification results. In order to validate the model performance, two publically available datasets (i.e., DEAP and SEED) are utilized. Employing these datasets, the proposed model achieves state-of-the-art results (93.1, 96.84%) for EEG signal classification to identify stress. EEG signals (dpeaa)DE-He213 Stress detection (dpeaa)DE-He213 Artifacts removal (dpeaa)DE-He213 Common spatial pattern (dpeaa)DE-He213 BiLSTM (dpeaa)DE-He213 Tubaishat, Abdallah aut Al-Obeidat, Feras aut Halim, Zahid (orcid)0000-0003-3094-3483 aut Tahir, Madiha aut Qayum, Fawad aut Enthalten in Soft Computing Springer-Verlag, 2003 26(2022), 20 vom: 23. Feb., Seite 10687-10698 (DE-627)SPR006469531 nnns volume:26 year:2022 number:20 day:23 month:02 pages:10687-10698 https://dx.doi.org/10.1007/s00500-022-06847-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2022 20 23 02 10687-10698 |
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10.1007/s00500-022-06847-w doi (DE-627)SPR048171964 (SPR)s00500-022-06847-w-e DE-627 ger DE-627 rakwb eng Rahman, Atta Ur verfasserin aut Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract Mental stress is an issue that creates functional limitations in the workplace. Chronic stress leads to a number of psychophysiological sicknesses. For instance, it raises the risk of depression, heart attack, and stroke. According to the most recent findings in neuroscience, the human brain is the primary focus of mental stress. Perception of biological motion in the human brain determines the risky and stressful situations. Neural signaling of the human brain is used as an objective measure for determining the stress level of a subject. The oscillations of electroencephalography (EEG) signals are utilized for classifying human stress. EEG signals have a higher temporal resolution and are rapidly distorted with unwanted noise, resulting in a variety of artifacts. This study utilizes Extended Independent Component Analysis based approach for artifacts removal. A Multiclass Common Spatial Pattern-based moving window technique is proposed here to obtain the most distinguishable time segment of EEG trials. BiLSTM is used to improve classification results. In order to validate the model performance, two publically available datasets (i.e., DEAP and SEED) are utilized. Employing these datasets, the proposed model achieves state-of-the-art results (93.1, 96.84%) for EEG signal classification to identify stress. EEG signals (dpeaa)DE-He213 Stress detection (dpeaa)DE-He213 Artifacts removal (dpeaa)DE-He213 Common spatial pattern (dpeaa)DE-He213 BiLSTM (dpeaa)DE-He213 Tubaishat, Abdallah aut Al-Obeidat, Feras aut Halim, Zahid (orcid)0000-0003-3094-3483 aut Tahir, Madiha aut Qayum, Fawad aut Enthalten in Soft Computing Springer-Verlag, 2003 26(2022), 20 vom: 23. Feb., Seite 10687-10698 (DE-627)SPR006469531 nnns volume:26 year:2022 number:20 day:23 month:02 pages:10687-10698 https://dx.doi.org/10.1007/s00500-022-06847-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 26 2022 20 23 02 10687-10698 |
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Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals |
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Abstract Mental stress is an issue that creates functional limitations in the workplace. Chronic stress leads to a number of psychophysiological sicknesses. For instance, it raises the risk of depression, heart attack, and stroke. According to the most recent findings in neuroscience, the human brain is the primary focus of mental stress. Perception of biological motion in the human brain determines the risky and stressful situations. Neural signaling of the human brain is used as an objective measure for determining the stress level of a subject. The oscillations of electroencephalography (EEG) signals are utilized for classifying human stress. EEG signals have a higher temporal resolution and are rapidly distorted with unwanted noise, resulting in a variety of artifacts. This study utilizes Extended Independent Component Analysis based approach for artifacts removal. A Multiclass Common Spatial Pattern-based moving window technique is proposed here to obtain the most distinguishable time segment of EEG trials. BiLSTM is used to improve classification results. In order to validate the model performance, two publically available datasets (i.e., DEAP and SEED) are utilized. Employing these datasets, the proposed model achieves state-of-the-art results (93.1, 96.84%) for EEG signal classification to identify stress. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstractGer |
Abstract Mental stress is an issue that creates functional limitations in the workplace. Chronic stress leads to a number of psychophysiological sicknesses. For instance, it raises the risk of depression, heart attack, and stroke. According to the most recent findings in neuroscience, the human brain is the primary focus of mental stress. Perception of biological motion in the human brain determines the risky and stressful situations. Neural signaling of the human brain is used as an objective measure for determining the stress level of a subject. The oscillations of electroencephalography (EEG) signals are utilized for classifying human stress. EEG signals have a higher temporal resolution and are rapidly distorted with unwanted noise, resulting in a variety of artifacts. This study utilizes Extended Independent Component Analysis based approach for artifacts removal. A Multiclass Common Spatial Pattern-based moving window technique is proposed here to obtain the most distinguishable time segment of EEG trials. BiLSTM is used to improve classification results. In order to validate the model performance, two publically available datasets (i.e., DEAP and SEED) are utilized. Employing these datasets, the proposed model achieves state-of-the-art results (93.1, 96.84%) for EEG signal classification to identify stress. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract Mental stress is an issue that creates functional limitations in the workplace. Chronic stress leads to a number of psychophysiological sicknesses. For instance, it raises the risk of depression, heart attack, and stroke. According to the most recent findings in neuroscience, the human brain is the primary focus of mental stress. Perception of biological motion in the human brain determines the risky and stressful situations. Neural signaling of the human brain is used as an objective measure for determining the stress level of a subject. The oscillations of electroencephalography (EEG) signals are utilized for classifying human stress. EEG signals have a higher temporal resolution and are rapidly distorted with unwanted noise, resulting in a variety of artifacts. This study utilizes Extended Independent Component Analysis based approach for artifacts removal. A Multiclass Common Spatial Pattern-based moving window technique is proposed here to obtain the most distinguishable time segment of EEG trials. BiLSTM is used to improve classification results. In order to validate the model performance, two publically available datasets (i.e., DEAP and SEED) are utilized. Employing these datasets, the proposed model achieves state-of-the-art results (93.1, 96.84%) for EEG signal classification to identify stress. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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| title_short |
Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals |
| url |
https://dx.doi.org/10.1007/s00500-022-06847-w |
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| author2 |
Tubaishat, Abdallah Al-Obeidat, Feras Halim, Zahid Tahir, Madiha Qayum, Fawad |
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Tubaishat, Abdallah Al-Obeidat, Feras Halim, Zahid Tahir, Madiha Qayum, Fawad |
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| doi_str |
10.1007/s00500-022-06847-w |
| up_date |
2024-07-03T17:29:56.939Z |
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