EEG Based Emotion Recognition Using Long Short Term Memory Network with Improved Rat Swarm Optimization Algorithm Article Swipe
Amrendra Tripathi
,
Tanupriya Choudhury
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18280/ria.370205
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18280/ria.370205
The automatic human Emotion Recognition (ER) based on Electroencephalography (EEG) signal has gained more attention among the researcher communities with a rapid growth of Human Computer Interaction (HCI).Most of the prior models have not focused on the context-information of the EEG signals.In this research manuscript, a novel automated model is implemented for improving ER using EEG signals.In the initial phase, the signals are acquired from an online database: Database for Emotion Analysis using Physiological Signal (DEAP).Then, the data denoising is carried-out by implementing Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) filters.These filters aim in eliminating the artifacts and noises in the acquired raw EEG signals, and further, the feature extraction is carried-out utilizing 20 statistical features that extracts discriminative feature information from the decomposed EEG signals.In the last phase, the Long Short Term Memory network (LSTM) is used for human ER as arousal or valence.Additionally, the optimal hyper-parameters of the LSTM network are selected by proposing the Improved Rat Swarm Optimization Algorithm (IRSOA).As denoted in the resulting and discussion section, the IRSOA-LSTM network achieved a mean accuracy of 84.89%, sensitivity of 86.95%, specificity of 86%, precision of 83.68%, and f1-score of 85.28% on the DEAP database.The simulation outcomes state that the proposed IRSOA-LSTM network is better than the existing machine-learning models.
Related Topics
Concepts
Term (time)
Computer science
Swarm behaviour
Long short term memory
Electroencephalography
Optimization algorithm
Pattern recognition (psychology)
Algorithm
Artificial intelligence
Emotion recognition
Speech recognition
Artificial neural network
Psychology
Mathematical optimization
Mathematics
Neuroscience
Recurrent neural network
Quantum mechanics
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18280/ria.370205
- https://www.iieta.org/download/file/fid/98137
- OA Status
- bronze
- Cited By
- 4
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378418333
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4378418333Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18280/ria.370205Digital Object Identifier
- Title
-
EEG Based Emotion Recognition Using Long Short Term Memory Network with Improved Rat Swarm Optimization AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-30Full publication date if available
- Authors
-
Amrendra Tripathi, Tanupriya ChoudhuryList of authors in order
- Landing page
-
https://doi.org/10.18280/ria.370205Publisher landing page
- PDF URL
-
https://www.iieta.org/download/file/fid/98137Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
- OA URL
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https://www.iieta.org/download/file/fid/98137Direct OA link when available
- Concepts
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Term (time), Computer science, Swarm behaviour, Long short term memory, Electroencephalography, Optimization algorithm, Pattern recognition (psychology), Algorithm, Artificial intelligence, Emotion recognition, Speech recognition, Artificial neural network, Psychology, Mathematical optimization, Mathematics, Neuroscience, Recurrent neural network, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.The | 0 |
| abstract_inverted_index.aim | 94 |
| abstract_inverted_index.and | 87, 99, 107, 169, 190 |
| abstract_inverted_index.are | 62, 154 |
| abstract_inverted_index.for | 51, 69, 140 |
| abstract_inverted_index.has | 11 |
| abstract_inverted_index.not | 33 |
| abstract_inverted_index.raw | 104 |
| abstract_inverted_index.the | 16, 29, 36, 39, 57, 60, 76, 97, 102, 109, 124, 128, 131, 147, 151, 158, 167, 172, 195, 202, 209 |
| abstract_inverted_index.(ER) | 5 |
| abstract_inverted_index.86%, | 186 |
| abstract_inverted_index.DEAP | 196 |
| abstract_inverted_index.LSTM | 152 |
| abstract_inverted_index.Long | 132 |
| abstract_inverted_index.Mode | 84, 89 |
| abstract_inverted_index.Term | 134 |
| abstract_inverted_index.data | 77 |
| abstract_inverted_index.from | 64, 123 |
| abstract_inverted_index.have | 32 |
| abstract_inverted_index.last | 129 |
| abstract_inverted_index.mean | 177 |
| abstract_inverted_index.more | 13 |
| abstract_inverted_index.than | 208 |
| abstract_inverted_index.that | 118, 201 |
| abstract_inverted_index.this | 42 |
| abstract_inverted_index.used | 139 |
| abstract_inverted_index.with | 19 |
| abstract_inverted_index.(EEG) | 9 |
| abstract_inverted_index.(EMD) | 86 |
| abstract_inverted_index.(VMD) | 91 |
| abstract_inverted_index.Human | 24 |
| abstract_inverted_index.Short | 133 |
| abstract_inverted_index.Swarm | 161 |
| abstract_inverted_index.among | 15 |
| abstract_inverted_index.based | 6 |
| abstract_inverted_index.human | 2, 141 |
| abstract_inverted_index.model | 48 |
| abstract_inverted_index.novel | 46 |
| abstract_inverted_index.prior | 30 |
| abstract_inverted_index.rapid | 21 |
| abstract_inverted_index.state | 200 |
| abstract_inverted_index.using | 54, 72 |
| abstract_inverted_index.(LSTM) | 137 |
| abstract_inverted_index.85.28% | 193 |
| abstract_inverted_index.Memory | 135 |
| abstract_inverted_index.Signal | 74 |
| abstract_inverted_index.better | 207 |
| abstract_inverted_index.gained | 12 |
| abstract_inverted_index.growth | 22 |
| abstract_inverted_index.models | 31 |
| abstract_inverted_index.noises | 100 |
| abstract_inverted_index.online | 66 |
| abstract_inverted_index.phase, | 59, 130 |
| abstract_inverted_index.signal | 10 |
| abstract_inverted_index.83.68%, | 189 |
| abstract_inverted_index.84.89%, | 180 |
| abstract_inverted_index.86.95%, | 183 |
| abstract_inverted_index.Emotion | 3, 70 |
| abstract_inverted_index.arousal | 144 |
| abstract_inverted_index.denoted | 165 |
| abstract_inverted_index.feature | 110, 121 |
| abstract_inverted_index.filters | 93 |
| abstract_inverted_index.focused | 34 |
| abstract_inverted_index.initial | 58 |
| abstract_inverted_index.models. | 212 |
| abstract_inverted_index.network | 136, 153, 174, 205 |
| abstract_inverted_index.optimal | 148 |
| abstract_inverted_index.signals | 61 |
| abstract_inverted_index.Analysis | 71 |
| abstract_inverted_index.Computer | 25 |
| abstract_inverted_index.Database | 68 |
| abstract_inverted_index.Improved | 159 |
| abstract_inverted_index.accuracy | 178 |
| abstract_inverted_index.achieved | 175 |
| abstract_inverted_index.acquired | 63, 103 |
| abstract_inverted_index.existing | 210 |
| abstract_inverted_index.extracts | 119 |
| abstract_inverted_index.f1-score | 191 |
| abstract_inverted_index.features | 117 |
| abstract_inverted_index.further, | 108 |
| abstract_inverted_index.outcomes | 199 |
| abstract_inverted_index.proposed | 203 |
| abstract_inverted_index.research | 43 |
| abstract_inverted_index.section, | 171 |
| abstract_inverted_index.selected | 155 |
| abstract_inverted_index.signals, | 106 |
| abstract_inverted_index.Algorithm | 163 |
| abstract_inverted_index.Empirical | 83 |
| abstract_inverted_index.artifacts | 98 |
| abstract_inverted_index.attention | 14 |
| abstract_inverted_index.automated | 47 |
| abstract_inverted_index.automatic | 1 |
| abstract_inverted_index.database: | 67 |
| abstract_inverted_index.denoising | 78 |
| abstract_inverted_index.improving | 52 |
| abstract_inverted_index.precision | 187 |
| abstract_inverted_index.proposing | 157 |
| abstract_inverted_index.resulting | 168 |
| abstract_inverted_index.utilizing | 114 |
| abstract_inverted_index.(HCI).Most | 27 |
| abstract_inverted_index.(IRSOA).As | 164 |
| abstract_inverted_index.IRSOA-LSTM | 173, 204 |
| abstract_inverted_index.decomposed | 125 |
| abstract_inverted_index.discussion | 170 |
| abstract_inverted_index.extraction | 111 |
| abstract_inverted_index.researcher | 17 |
| abstract_inverted_index.signals.In | 41, 56, 127 |
| abstract_inverted_index.simulation | 198 |
| abstract_inverted_index.Interaction | 26 |
| abstract_inverted_index.Recognition | 4 |
| abstract_inverted_index.Variational | 88 |
| abstract_inverted_index.carried-out | 80, 113 |
| abstract_inverted_index.communities | 18 |
| abstract_inverted_index.eliminating | 96 |
| abstract_inverted_index.implemented | 50 |
| abstract_inverted_index.information | 122 |
| abstract_inverted_index.manuscript, | 44 |
| abstract_inverted_index.sensitivity | 181 |
| abstract_inverted_index.specificity | 184 |
| abstract_inverted_index.statistical | 116 |
| abstract_inverted_index.(DEAP).Then, | 75 |
| abstract_inverted_index.Optimization | 162 |
| abstract_inverted_index.database.The | 197 |
| abstract_inverted_index.implementing | 82 |
| abstract_inverted_index.Decomposition | 85, 90 |
| abstract_inverted_index.Physiological | 73 |
| abstract_inverted_index.filters.These | 92 |
| abstract_inverted_index.discriminative | 120 |
| abstract_inverted_index.hyper-parameters | 149 |
| abstract_inverted_index.machine-learning | 211 |
| abstract_inverted_index.context-information | 37 |
| abstract_inverted_index.valence.Additionally, | 146 |
| abstract_inverted_index.Electroencephalography | 8 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5057757150 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I5847235 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.4000000059604645 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.67247249 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |