Brain Computer Interface for Emotion Recognition Based on EEG Signal
This paper presents an emotion recognition system based on electroencephalography (EEG) signals. This system helps medical practitioners to analyse the mental health of an individual. Eight healthy volunteers/ subjects had participated in this experiment. A specific feeling is evoked using particula...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2023/03/itmconf_icdsia2023_01001.pdf |
| _version_ | 1797807683896606720 |
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| author | Shilaskar Swati Bhatlawande Shripad Kulkarni Rohan Lonkar Tanmay |
| author_facet | Shilaskar Swati Bhatlawande Shripad Kulkarni Rohan Lonkar Tanmay |
| author_sort | Shilaskar Swati |
| collection | DOAJ |
| description | This paper presents an emotion recognition system based on electroencephalography (EEG) signals. This system helps medical practitioners to analyse the mental health of an individual. Eight healthy volunteers/ subjects had participated in this experiment. A specific feeling is evoked using particular songs and videos that are collected to present before the subjects. Total 6 emotions namely neutral, happy, sad, disgust, fear and motivate are captured and analysed. Data is classified using eighteen statistical features. The sampling rate is 1200Hz. Signals are filtered using pre-processing techniques. Frequency, time and timefrequency domain features are extracted. An array of 10 classifiers is used including Decision Tree, Random Forest, Optimised Random Forest, Logistic regression, Support Vector Machine (SVM) Polynomial, SVM Sigmoid, SVM RBF, K-Nearest Neighbours, Gaussian NB, Gradient Boosting Classifier. Accuracy, recall, precision, and F1 score are employed as performance metrics. The accuracy obtained for SVM classifier was 79.34%. |
| first_indexed | 2024-03-13T06:26:20Z |
| format | Article |
| id | doaj.art-e61ac2289726474d984862578403408b |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-03-13T06:26:20Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-e61ac2289726474d984862578403408b2023-06-09T09:24:03ZengEDP SciencesITM Web of Conferences2271-20972023-01-01530100110.1051/itmconf/20235301001itmconf_icdsia2023_01001Brain Computer Interface for Emotion Recognition Based on EEG SignalShilaskar Swati0Bhatlawande Shripad1Kulkarni Rohan2Lonkar Tanmay3Savitribai Phule Pune UniversitySavitribai Phule Pune UniversitySavitribai Phule Pune UniversitySavitribai Phule Pune UniversityThis paper presents an emotion recognition system based on electroencephalography (EEG) signals. This system helps medical practitioners to analyse the mental health of an individual. Eight healthy volunteers/ subjects had participated in this experiment. A specific feeling is evoked using particular songs and videos that are collected to present before the subjects. Total 6 emotions namely neutral, happy, sad, disgust, fear and motivate are captured and analysed. Data is classified using eighteen statistical features. The sampling rate is 1200Hz. Signals are filtered using pre-processing techniques. Frequency, time and timefrequency domain features are extracted. An array of 10 classifiers is used including Decision Tree, Random Forest, Optimised Random Forest, Logistic regression, Support Vector Machine (SVM) Polynomial, SVM Sigmoid, SVM RBF, K-Nearest Neighbours, Gaussian NB, Gradient Boosting Classifier. Accuracy, recall, precision, and F1 score are employed as performance metrics. The accuracy obtained for SVM classifier was 79.34%.https://www.itm-conferences.org/articles/itmconf/pdf/2023/03/itmconf_icdsia2023_01001.pdf |
| spellingShingle | Shilaskar Swati Bhatlawande Shripad Kulkarni Rohan Lonkar Tanmay Brain Computer Interface for Emotion Recognition Based on EEG Signal ITM Web of Conferences |
| title | Brain Computer Interface for Emotion Recognition Based on EEG Signal |
| title_full | Brain Computer Interface for Emotion Recognition Based on EEG Signal |
| title_fullStr | Brain Computer Interface for Emotion Recognition Based on EEG Signal |
| title_full_unstemmed | Brain Computer Interface for Emotion Recognition Based on EEG Signal |
| title_short | Brain Computer Interface for Emotion Recognition Based on EEG Signal |
| title_sort | brain computer interface for emotion recognition based on eeg signal |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2023/03/itmconf_icdsia2023_01001.pdf |
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