EEG-Based Emotion Classification Using Stacking Ensemble Approach
Rapid advancements in the medical field have drawn much attention to automatic emotion classification from EEG data. People’s emotional states are crucial factors in how they behave and interact physiologically. The diagnosis of patients’ mental disorders is one potential medical use. When feeling w...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/21/8550 |
| _version_ | 1827645450453254144 |
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| author | Subhajit Chatterjee Yung-Cheol Byun |
| author_facet | Subhajit Chatterjee Yung-Cheol Byun |
| author_sort | Subhajit Chatterjee |
| collection | DOAJ |
| description | Rapid advancements in the medical field have drawn much attention to automatic emotion classification from EEG data. People’s emotional states are crucial factors in how they behave and interact physiologically. The diagnosis of patients’ mental disorders is one potential medical use. When feeling well, people work and communicate more effectively. Negative emotions can be detrimental to both physical and mental health. Many earlier studies that investigated the use of the electroencephalogram (EEG) for emotion classification have focused on collecting data from the whole brain because of the rapidly developing science of machine learning. However, researchers cannot understand how various emotional states and EEG traits are related. This work seeks to classify EEG signals’ positive, negative, and neutral emotional states by using a stacking-ensemble-based classification model that boosts accuracy to increase the efficacy of emotion classification using EEG. The selected features are used to train a model that was created using a random forest, light gradient boosting machine, and gradient-boosting-based stacking ensemble classifier (RLGB-SE), where the base classifiers random forest (RF), light gradient boosting machine (LightGBM), and gradient boosting classifier (GBC) were used at level 0. The meta classifier (RF) at level 1 is trained using the results from each base classifier to acquire the final predictions. The suggested ensemble model achieves a greater classification accuracy of 99.55%. Additionally, while comparing performance indices, the suggested technique outperforms as compared with the base classifiers. Comparing the proposed stacking strategy to state-of-the-art techniques, it can be seen that the performance for emotion categorization is promising. |
| first_indexed | 2024-03-09T18:39:28Z |
| format | Article |
| id | doaj.art-bc758fe0de4d4e6f9741bbdc5ff805bb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T18:39:28Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bc758fe0de4d4e6f9741bbdc5ff805bb2023-11-24T06:49:51ZengMDPI AGSensors1424-82202022-11-012221855010.3390/s22218550EEG-Based Emotion Classification Using Stacking Ensemble ApproachSubhajit Chatterjee0Yung-Cheol Byun1Department of Computer Engineering, Jeju National University, Jeju 63243, KoreaDepartment of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, KoreaRapid advancements in the medical field have drawn much attention to automatic emotion classification from EEG data. People’s emotional states are crucial factors in how they behave and interact physiologically. The diagnosis of patients’ mental disorders is one potential medical use. When feeling well, people work and communicate more effectively. Negative emotions can be detrimental to both physical and mental health. Many earlier studies that investigated the use of the electroencephalogram (EEG) for emotion classification have focused on collecting data from the whole brain because of the rapidly developing science of machine learning. However, researchers cannot understand how various emotional states and EEG traits are related. This work seeks to classify EEG signals’ positive, negative, and neutral emotional states by using a stacking-ensemble-based classification model that boosts accuracy to increase the efficacy of emotion classification using EEG. The selected features are used to train a model that was created using a random forest, light gradient boosting machine, and gradient-boosting-based stacking ensemble classifier (RLGB-SE), where the base classifiers random forest (RF), light gradient boosting machine (LightGBM), and gradient boosting classifier (GBC) were used at level 0. The meta classifier (RF) at level 1 is trained using the results from each base classifier to acquire the final predictions. The suggested ensemble model achieves a greater classification accuracy of 99.55%. Additionally, while comparing performance indices, the suggested technique outperforms as compared with the base classifiers. Comparing the proposed stacking strategy to state-of-the-art techniques, it can be seen that the performance for emotion categorization is promising.https://www.mdpi.com/1424-8220/22/21/8550deep learningemotion classificationEEG datastacking ensemble classifierrandom forestlightGBM |
| spellingShingle | Subhajit Chatterjee Yung-Cheol Byun EEG-Based Emotion Classification Using Stacking Ensemble Approach Sensors deep learning emotion classification EEG data stacking ensemble classifier random forest lightGBM |
| title | EEG-Based Emotion Classification Using Stacking Ensemble Approach |
| title_full | EEG-Based Emotion Classification Using Stacking Ensemble Approach |
| title_fullStr | EEG-Based Emotion Classification Using Stacking Ensemble Approach |
| title_full_unstemmed | EEG-Based Emotion Classification Using Stacking Ensemble Approach |
| title_short | EEG-Based Emotion Classification Using Stacking Ensemble Approach |
| title_sort | eeg based emotion classification using stacking ensemble approach |
| topic | deep learning emotion classification EEG data stacking ensemble classifier random forest lightGBM |
| url | https://www.mdpi.com/1424-8220/22/21/8550 |
| work_keys_str_mv | AT subhajitchatterjee eegbasedemotionclassificationusingstackingensembleapproach AT yungcheolbyun eegbasedemotionclassificationusingstackingensembleapproach |