Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet

Kumari, Nandini; Anwar, Shamama; Bhattacherjee, Vandana

doi:10.1007/s00521-022-06942-x

Cited by 14 publications

(4 citation statements)

References 42 publications

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“…For example, Kumari et al used the short-term Fourier transform algorithm to transform raw one-dimensional EEG signals into a two-dimensional spectrogram image and implemented a capsule network to process the spatio-temporal characteristics of EEG signals. The average accuracy of valence, arousal, and dominance on the DEAP dataset is 77.50%, 78.44%, and 79.38% respectively [ 13 ]. Deng et al used a capsule network to extract the spatial features of EEG channels, combined with the attention mechanism to adaptively assign different weights to each EEG channel, and used LSTM to extract the temporal features of EEG sequences.…”

Section: Related Workmentioning

confidence: 99%

Emotion Classification from Multi-Band Electroencephalogram Data Using Dynamic Simplifying Graph Convolutional Network and Channel Style Recalibration Module

Zhu

Liu

Zhao

et al. 2023

Sensors

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Because of its ability to objectively reflect people’s emotional states, electroencephalogram (EEG) has been attracting increasing research attention for emotion classification. The classification method based on spatial-domain analysis is one of the research hotspots. However, most previous studies ignored the complementarity of information between different frequency bands, and the information in a single frequency band is not fully mined, which increases the computational time and the difficulty of improving classification accuracy. To address the above problems, this study proposes an emotion classification method based on dynamic simplifying graph convolutional (SGC) networks and a style recalibration module (SRM) for channels, termed SGC-SRM, with multi-band EEG data as input. Specifically, first, the graph structure is constructed using the differential entropy characteristics of each sub-band and the internal relationship between different channels is dynamically learned through SGC networks. Second, a convolution layer based on the SRM is introduced to recalibrate channel features to extract more emotion-related features. Third, the extracted sub-band features are fused at the feature level and classified. In addition, to reduce the redundant information between EEG channels and the computational time, (1) we adopt only 12 channels that are suitable for emotion classification to optimize the recognition algorithm, which can save approximately 90.5% of the time cost compared with using all channels; (2) we adopt information in the θ, α, β, and γ bands, consequently saving 23.3% of the time consumed compared with that in the full bands while maintaining almost the same level of classification accuracy. Finally, a subject-independent experiment is conducted on the public SEED dataset using the leave-one-subject-out cross-validation strategy. According to experimental results, SGC-SRM improves classification accuracy by 5.51–15.43% compared with existing methods.

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Section: Related Workmentioning

confidence: 99%

Emotion Classification from Multi-Band Electroencephalogram Data Using Dynamic Simplifying Graph Convolutional Network and Channel Style Recalibration Module

Zhu

Liu

Zhao

et al. 2023

Sensors

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“…Unlike facial emotion recognition, which primarily uses images, the data sources for EEG-based emotion detection are more intricate and varied. A notable observation is that most EEG-based emotion recognition studies rely on pre-existing datasets [such as SEED, DEAP (Koelstra et al, 2011 ), DREAMER (Katsigiannis and Ramzan, 2017 )] rather than collecting data independently (Kumari et al, 2022 ). Furthermore, there seems to be a scarcity of research in applying self-designed and trained models to real-world emotion measurement experiments.…”

Section: The Challenge Of Applicationmentioning

confidence: 99%

Mini review: Challenges in EEG emotion recognition

Zhang,

Fort,

Giménez Mateu

2024

Front. Psychol.

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Electroencephalography (EEG) stands as a pioneering tool at the intersection of neuroscience and technology, offering unprecedented insights into human emotions. Through this comprehensive review, we explore the challenges and opportunities associated with EEG-based emotion recognition. While recent literature suggests promising high accuracy rates, these claims necessitate critical scrutiny for their authenticity and applicability. The article highlights the significant challenges in generalizing findings from a multitude of EEG devices and data sources, as well as the difficulties in data collection. Furthermore, the disparity between controlled laboratory settings and genuine emotional experiences presents a paradox within the paradigm of emotion research. We advocate for a balanced approach, emphasizing the importance of critical evaluation, methodological standardization, and acknowledging the dynamism of emotions for a more holistic understanding of the human emotional landscape.

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“…Time series data have distinctive features that distinguish them from other data types. In particular, temporal dependence is a key feature in which each data point is influenced by its previous observations [14]. This interdependence leads to discernible patterns and trends within the data, requiring effective capture and analysis techniques.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Automated Machine Learning (AutoML) Tools for Epileptic Seizure Detection Using Electroencephalograms (EEG)

Lenkala,

Marry,

Gopovaram

et al. 2023

Computers

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Epilepsy is a neurological disease characterized by recurrent seizures caused by abnormal electrical activity in the brain. One of the methods used to diagnose epilepsy is through electroencephalogram (EEG) analysis. EEG is a non-invasive medical test for quantifying electrical activity in the brain. Applying machine learning (ML) to EEG data for epilepsy diagnosis has the potential to be more accurate and efficient. However, expert knowledge is required to set up the ML model with correct hyperparameters. Automated machine learning (AutoML) tools aim to make ML more accessible to non-experts and automate many ML processes to create a high-performing ML model. This article explores the use of automated machine learning (AutoML) tools for diagnosing epilepsy using electroencephalogram (EEG) data. The study compares the performance of three different AutoML tools, AutoGluon, Auto-Sklearn, and Amazon Sagemaker, on three different datasets from the UC Irvine ML Repository, Bonn EEG time series dataset, and Zenodo. Performance measures used for evaluation include accuracy, F1 score, recall, and precision. The results show that all three AutoML tools were able to generate high-performing ML models for the diagnosis of epilepsy. The generated ML models perform better when the training dataset is larger in size. Amazon Sagemaker and Auto-Sklearn performed better with smaller datasets. This is the first study to compare several AutoML tools and shows that AutoML tools can be utilized to create well-performing solutions for the diagnosis of epilepsy via processing hard-to-analyze EEG timeseries data.

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Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet

Cited by 14 publications

References 42 publications

Emotion Classification from Multi-Band Electroencephalogram Data Using Dynamic Simplifying Graph Convolutional Network and Channel Style Recalibration Module

Emotion Classification from Multi-Band Electroencephalogram Data Using Dynamic Simplifying Graph Convolutional Network and Channel Style Recalibration Module

Mini review: Challenges in EEG emotion recognition

Comparison of Automated Machine Learning (AutoML) Tools for Epileptic Seizure Detection Using Electroencephalograms (EEG)

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