Brain lateralisation feature extraction and ant colony optimisation‐bidirectional LSTM network model for emotion recognition
Abstract The human inner perception is the core technology for human‐robot interactions. Emotion recognition has been established as a representative study for recognising the internal state of human beings in addition to research regarding intention recognition. In the study of emotion recognition...
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| Format: | Article |
| Language: | English |
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Wiley
2022-02-01
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| Series: | IET Signal Processing |
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| Online Access: | https://doi.org/10.1049/sil2.12076 |
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| author | Woo‐Hyun Hwang Dong‐Hyun Kang Deok‐Hwan Kim |
| author_facet | Woo‐Hyun Hwang Dong‐Hyun Kang Deok‐Hwan Kim |
| author_sort | Woo‐Hyun Hwang |
| collection | DOAJ |
| description | Abstract The human inner perception is the core technology for human‐robot interactions. Emotion recognition has been established as a representative study for recognising the internal state of human beings in addition to research regarding intention recognition. In the study of emotion recognition using EEG signals, emotion evaluation is categorised into two methods: discrete emotion and continuous emotion. This study proposes an ant colony optimisation‐bidirectional LSTM network model. Unlike other LSTM network models, this model improves performance by applying more weight values that are valid for emotion recognition in the current LSTM cell state using past and future biosignal information and combining ACO to find the optimal combination of emotion recognition features among many features. Furthermore, it extracts valid features using peripheral nervous system signals PPG, GSR, and EOG as well as the central nervous system signals EEG, simultaneously. The authors reinforce feature performance by adding brain lateralisation for emotion recognition. Emotion label data were recorded by performing annotation labelling, in which the values are between −100 and 100 in the arousal‐valence domain. The experimental result shows that the ACO‐bidirectional LSTM model using brain lateralisation in the MAHNOB‐HCI, DEAP, MERTI‐Apps database yields the best valence performance, RMSE of 0.0442, 0.0523, and 0.0568, respectively. |
| format | Article |
| id | doaj-art-0b65c97d6d044e788b8293777e63dbbf |
| institution | Kabale University |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2022-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-0b65c97d6d044e788b8293777e63dbbf2025-02-03T01:29:36ZengWileyIET Signal Processing1751-96751751-96832022-02-01161456110.1049/sil2.12076Brain lateralisation feature extraction and ant colony optimisation‐bidirectional LSTM network model for emotion recognitionWoo‐Hyun Hwang0Dong‐Hyun Kang1Deok‐Hwan Kim2Department of Electronic Engineering Inha University Incheon Republic of KoreaDepartment of Electronic Engineering Inha University Incheon Republic of KoreaDepartment of Electronic Engineering Inha University Incheon Republic of KoreaAbstract The human inner perception is the core technology for human‐robot interactions. Emotion recognition has been established as a representative study for recognising the internal state of human beings in addition to research regarding intention recognition. In the study of emotion recognition using EEG signals, emotion evaluation is categorised into two methods: discrete emotion and continuous emotion. This study proposes an ant colony optimisation‐bidirectional LSTM network model. Unlike other LSTM network models, this model improves performance by applying more weight values that are valid for emotion recognition in the current LSTM cell state using past and future biosignal information and combining ACO to find the optimal combination of emotion recognition features among many features. Furthermore, it extracts valid features using peripheral nervous system signals PPG, GSR, and EOG as well as the central nervous system signals EEG, simultaneously. The authors reinforce feature performance by adding brain lateralisation for emotion recognition. Emotion label data were recorded by performing annotation labelling, in which the values are between −100 and 100 in the arousal‐valence domain. The experimental result shows that the ACO‐bidirectional LSTM model using brain lateralisation in the MAHNOB‐HCI, DEAP, MERTI‐Apps database yields the best valence performance, RMSE of 0.0442, 0.0523, and 0.0568, respectively.https://doi.org/10.1049/sil2.12076ant colony optimisationbidirectional‐LSTM networkbrain lateralisationemotion recognition |
| spellingShingle | Woo‐Hyun Hwang Dong‐Hyun Kang Deok‐Hwan Kim Brain lateralisation feature extraction and ant colony optimisation‐bidirectional LSTM network model for emotion recognition IET Signal Processing ant colony optimisation bidirectional‐LSTM network brain lateralisation emotion recognition |
| title | Brain lateralisation feature extraction and ant colony optimisation‐bidirectional LSTM network model for emotion recognition |
| title_full | Brain lateralisation feature extraction and ant colony optimisation‐bidirectional LSTM network model for emotion recognition |
| title_fullStr | Brain lateralisation feature extraction and ant colony optimisation‐bidirectional LSTM network model for emotion recognition |
| title_full_unstemmed | Brain lateralisation feature extraction and ant colony optimisation‐bidirectional LSTM network model for emotion recognition |
| title_short | Brain lateralisation feature extraction and ant colony optimisation‐bidirectional LSTM network model for emotion recognition |
| title_sort | brain lateralisation feature extraction and ant colony optimisation bidirectional lstm network model for emotion recognition |
| topic | ant colony optimisation bidirectional‐LSTM network brain lateralisation emotion recognition |
| url | https://doi.org/10.1049/sil2.12076 |
| work_keys_str_mv | AT woohyunhwang brainlateralisationfeatureextractionandantcolonyoptimisationbidirectionallstmnetworkmodelforemotionrecognition AT donghyunkang brainlateralisationfeatureextractionandantcolonyoptimisationbidirectionallstmnetworkmodelforemotionrecognition AT deokhwankim brainlateralisationfeatureextractionandantcolonyoptimisationbidirectionallstmnetworkmodelforemotionrecognition |