Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals
Emotion recognition is very important for the humans in order to enhance the self-awareness and react correctly to the actions around them. Based on the complication and series of emotions, EEG-enabled emotion recognition is still a difficult issue. Hence, an effective human recognition approach is...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | International Journal of Telemedicine and Applications |
| Online Access: | http://dx.doi.org/10.1155/2022/3749413 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832548598821158912 |
|---|---|
| author | K. S. Bhanumathi D. Jayadevappa Satish Tunga |
| author_facet | K. S. Bhanumathi D. Jayadevappa Satish Tunga |
| author_sort | K. S. Bhanumathi |
| collection | DOAJ |
| description | Emotion recognition is very important for the humans in order to enhance the self-awareness and react correctly to the actions around them. Based on the complication and series of emotions, EEG-enabled emotion recognition is still a difficult issue. Hence, an effective human recognition approach is designed using the proposed feedback artificial shuffled shepherd optimization- (FASSO-) based deep maxout network (DMN) for recognizing emotions using EEG signals. The proposed technique incorporates feedback artificial tree (FAT) algorithm and shuffled shepherd optimization algorithm (SSOA). Here, median filter is used for preprocessing to remove the noise present in the EEG signals. The features, like DWT, spectral flatness, logarithmic band power, fluctuation index, spectral decrease, spectral roll-off, and relative energy, are extracted to perform further processing. Based on the data augmented results, emotion recognition can be accomplished using the DMN, where the training process of the DMN is performed using the proposed FASSO method. Furthermore, the experimental results and performance analysis of the proposed algorithm provide efficient performance with respect to accuracy, specificity, and sensitivity with the maximal values of 0.889, 0.89, and 0.886, respectively. |
| format | Article |
| id | doaj-art-3ef0e34220374607be398ad68708a948 |
| institution | Kabale University |
| issn | 1687-6423 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Telemedicine and Applications |
| spelling | doaj-art-3ef0e34220374607be398ad68708a9482025-02-03T06:13:36ZengWileyInternational Journal of Telemedicine and Applications1687-64232022-01-01202210.1155/2022/3749413Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG SignalsK. S. Bhanumathi0D. Jayadevappa1Satish Tunga2Department of Electronics and Instrumentation EngineeringDepartment of Electronics and Instrumentation EngineeringDepartment of Electronics & Telecommunication EngineeringEmotion recognition is very important for the humans in order to enhance the self-awareness and react correctly to the actions around them. Based on the complication and series of emotions, EEG-enabled emotion recognition is still a difficult issue. Hence, an effective human recognition approach is designed using the proposed feedback artificial shuffled shepherd optimization- (FASSO-) based deep maxout network (DMN) for recognizing emotions using EEG signals. The proposed technique incorporates feedback artificial tree (FAT) algorithm and shuffled shepherd optimization algorithm (SSOA). Here, median filter is used for preprocessing to remove the noise present in the EEG signals. The features, like DWT, spectral flatness, logarithmic band power, fluctuation index, spectral decrease, spectral roll-off, and relative energy, are extracted to perform further processing. Based on the data augmented results, emotion recognition can be accomplished using the DMN, where the training process of the DMN is performed using the proposed FASSO method. Furthermore, the experimental results and performance analysis of the proposed algorithm provide efficient performance with respect to accuracy, specificity, and sensitivity with the maximal values of 0.889, 0.89, and 0.886, respectively.http://dx.doi.org/10.1155/2022/3749413 |
| spellingShingle | K. S. Bhanumathi D. Jayadevappa Satish Tunga Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals International Journal of Telemedicine and Applications |
| title | Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals |
| title_full | Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals |
| title_fullStr | Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals |
| title_full_unstemmed | Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals |
| title_short | Feedback Artificial Shuffled Shepherd Optimization-Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals |
| title_sort | feedback artificial shuffled shepherd optimization based deep maxout network for human emotion recognition using eeg signals |
| url | http://dx.doi.org/10.1155/2022/3749413 |
| work_keys_str_mv | AT ksbhanumathi feedbackartificialshuffledshepherdoptimizationbaseddeepmaxoutnetworkforhumanemotionrecognitionusingeegsignals AT djayadevappa feedbackartificialshuffledshepherdoptimizationbaseddeepmaxoutnetworkforhumanemotionrecognitionusingeegsignals AT satishtunga feedbackartificialshuffledshepherdoptimizationbaseddeepmaxoutnetworkforhumanemotionrecognitionusingeegsignals |