A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG
Electroencephalography (EEG)-based emotion recognition technologies can effectively help robots to perceive human behavior, which have attracted extensive attention in human–machine interaction (HMI). Due to the complexity of EEG data, current researchers tend to extract different types of hand-craf...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/3/1954 |
| _version_ | 1797625051468529664 |
|---|---|
| author | Zhipeng Zhang Liyi Zhang |
| author_facet | Zhipeng Zhang Liyi Zhang |
| author_sort | Zhipeng Zhang |
| collection | DOAJ |
| description | Electroencephalography (EEG)-based emotion recognition technologies can effectively help robots to perceive human behavior, which have attracted extensive attention in human–machine interaction (HMI). Due to the complexity of EEG data, current researchers tend to extract different types of hand-crafted features and connect all frequency bands for further study. However, this may result in the loss of some discriminative information of frequency band combinations and make the classification models unable to obtain the best results. In order to recognize emotions accurately, this paper designs a novel EEG-based emotion recognition framework using complementary information of frequency bands. First, after the features of the preprocessed EEG data are extracted, the combinations of all the adjacent frequency bands in different scales are obtained through permutation and reorganization. Subsequently, the improved classification method, homogeneous-collaboration-representation-based classification, is used to obtain the classification results of each combination. Finally, the circular multi-grained ensemble learning method is put forward to re-exact the characteristics of each result and merge the machine learning methods and simple majority voting for the decision fusion. In the experiment, the classification accuracies of our framework in arousal and valence on the DEAP database are 95.09% and 94.38% respectively, and that in the four classification problems on the SEED IV database is 96.37%. |
| first_indexed | 2024-03-11T09:51:21Z |
| format | Article |
| id | doaj.art-b592bc8667a2402fac48aa8e239f0f05 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:51:21Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b592bc8667a2402fac48aa8e239f0f052023-11-16T16:12:39ZengMDPI AGApplied Sciences2076-34172023-02-01133195410.3390/app13031954A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEGZhipeng Zhang0Liyi Zhang1School of Information and Management, Wuhan University, No. 16, Luojiashan Road, Wuchang District, Wuhan 430072, ChinaSchool of Information and Management, Wuhan University, No. 16, Luojiashan Road, Wuchang District, Wuhan 430072, ChinaElectroencephalography (EEG)-based emotion recognition technologies can effectively help robots to perceive human behavior, which have attracted extensive attention in human–machine interaction (HMI). Due to the complexity of EEG data, current researchers tend to extract different types of hand-crafted features and connect all frequency bands for further study. However, this may result in the loss of some discriminative information of frequency band combinations and make the classification models unable to obtain the best results. In order to recognize emotions accurately, this paper designs a novel EEG-based emotion recognition framework using complementary information of frequency bands. First, after the features of the preprocessed EEG data are extracted, the combinations of all the adjacent frequency bands in different scales are obtained through permutation and reorganization. Subsequently, the improved classification method, homogeneous-collaboration-representation-based classification, is used to obtain the classification results of each combination. Finally, the circular multi-grained ensemble learning method is put forward to re-exact the characteristics of each result and merge the machine learning methods and simple majority voting for the decision fusion. In the experiment, the classification accuracies of our framework in arousal and valence on the DEAP database are 95.09% and 94.38% respectively, and that in the four classification problems on the SEED IV database is 96.37%.https://www.mdpi.com/2076-3417/13/3/1954electroencephalogramemotion recognitionhomogeneous collaboration representationcircular multi-grained scanningensemble learning |
| spellingShingle | Zhipeng Zhang Liyi Zhang A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG Applied Sciences electroencephalogram emotion recognition homogeneous collaboration representation circular multi-grained scanning ensemble learning |
| title | A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG |
| title_full | A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG |
| title_fullStr | A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG |
| title_full_unstemmed | A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG |
| title_short | A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG |
| title_sort | two step framework to recognize emotion using the combinations of adjacent frequency bands of eeg |
| topic | electroencephalogram emotion recognition homogeneous collaboration representation circular multi-grained scanning ensemble learning |
| url | https://www.mdpi.com/2076-3417/13/3/1954 |
| work_keys_str_mv | AT zhipengzhang atwostepframeworktorecognizeemotionusingthecombinationsofadjacentfrequencybandsofeeg AT liyizhang atwostepframeworktorecognizeemotionusingthecombinationsofadjacentfrequencybandsofeeg AT zhipengzhang twostepframeworktorecognizeemotionusingthecombinationsofadjacentfrequencybandsofeeg AT liyizhang twostepframeworktorecognizeemotionusingthecombinationsofadjacentfrequencybandsofeeg |