An overlapping sliding window and combined features based emotion recognition system for EEG signals
Purpose – The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data. Design/methodology/approach – Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other...
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Emerald Publishing
2025-01-01
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| Series: | Applied Computing and Informatics |
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| Online Access: | https://www.emerald.com/insight/content/doi/10.1108/ACI-05-2021-0130/full/pdf |
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| author | Shruti Garg Rahul Kumar Patro Soumyajit Behera Neha Prerna Tigga Ranjita Pandey |
| author_facet | Shruti Garg Rahul Kumar Patro Soumyajit Behera Neha Prerna Tigga Ranjita Pandey |
| author_sort | Shruti Garg |
| collection | DOAJ |
| description | Purpose – The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data. Design/methodology/approach – Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features. Findings – The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches. Originality/value – The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model. |
| format | Article |
| id | doaj-art-4482253e0b01424d8bd6d5d550be81ac |
| institution | Kabale University |
| issn | 2634-1964 2210-8327 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Emerald Publishing |
| record_format | Article |
| series | Applied Computing and Informatics |
| spelling | doaj-art-4482253e0b01424d8bd6d5d550be81ac2025-01-28T12:19:18ZengEmerald PublishingApplied Computing and Informatics2634-19642210-83272025-01-01211/211413010.1108/ACI-05-2021-0130An overlapping sliding window and combined features based emotion recognition system for EEG signalsShruti Garg0Rahul Kumar Patro1Soumyajit Behera2Neha Prerna Tigga3Ranjita Pandey4Birla Institute of Technology, Ranchi, IndiaBirla Institute of Technology, Ranchi, IndiaBirla Institute of Technology, Ranchi, IndiaBirla Institute of Technology, Ranchi, IndiaUniversity of Delhi, New Delhi, IndiaPurpose – The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data. Design/methodology/approach – Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features. Findings – The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches. Originality/value – The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.https://www.emerald.com/insight/content/doi/10.1108/ACI-05-2021-0130/full/pdfElectroencephalography (EEG)Emotion recognition (ER)1D and 2D convolution neural network (CNN) |
| spellingShingle | Shruti Garg Rahul Kumar Patro Soumyajit Behera Neha Prerna Tigga Ranjita Pandey An overlapping sliding window and combined features based emotion recognition system for EEG signals Applied Computing and Informatics Electroencephalography (EEG) Emotion recognition (ER) 1D and 2D convolution neural network (CNN) |
| title | An overlapping sliding window and combined features based emotion recognition system for EEG signals |
| title_full | An overlapping sliding window and combined features based emotion recognition system for EEG signals |
| title_fullStr | An overlapping sliding window and combined features based emotion recognition system for EEG signals |
| title_full_unstemmed | An overlapping sliding window and combined features based emotion recognition system for EEG signals |
| title_short | An overlapping sliding window and combined features based emotion recognition system for EEG signals |
| title_sort | overlapping sliding window and combined features based emotion recognition system for eeg signals |
| topic | Electroencephalography (EEG) Emotion recognition (ER) 1D and 2D convolution neural network (CNN) |
| url | https://www.emerald.com/insight/content/doi/10.1108/ACI-05-2021-0130/full/pdf |
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