Recognition of persisting emotional valence from EEG using convolutional neural networks
Abstract
Recently there has been considerable interest in EEG-based emotion recognition (EEG-ER), which is one of the utilization of BCI. However, it is not easy to realize the EEG-ER system which can recognize emotions with high accuracy because of the tendency for important information in EEG signals to be concealed by noises. Deep learning is the golden tool to grasp the features concealed in EEG data and enable highly accurate EEG-ER because deep neural networks (DNNs) may have higher recognition capability than humans'. The publicly available dataset named DEAP, which is for emotion analysis using EEG, was used in the experiment. The CNN and a conventional model used for comparison are evaluated by the tests according to 11-fold cross validation scheme. EEG raw data obtained from 16 electrodes without general preprocesses were used as input data. The models classify and recognize EEG signals according to the emotional states "positive" or "negative" which were caused by watching music videos. The results show that the more training data are, the much higher the accuracies of CNNs are (by over 20%). It also suggests that the increased training data need not to belong to the same person's EEG data as the test data so as to get the CNN recognizing emotions accurately. The results indicate that there are not only the considerable amount of the interpersonal difference but also commonality of EEG properties.
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Yanagimoto and Sugimoto [145] divided raw 16-channel EEG data into 1s segments and used a seven-layer CNN with 10 ms kernels on the first layer, leading to accuracy improvements of over 20%.
“For example, Yanagimoto and Sugimoto [145] divided the raw 16-channel EEG data into 1s segments and used a seven-layer CNN with 10 ms kernels on the first layer, leading to accuracy improvements of over 20%.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [145]
All four identified studies achieved accuracy improvements over handcrafted approaches, but a lack of training data was also mentioned.
“All achieved accuracy improvements over handcrafted approaches, but a lack of training data was also mentioned [145], [146].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [145]
Yanagimoto and Sugimoto used a CNN on raw 16-channel EEG data to differentiate between positive and negative affective states, and this approach outperformed shallow models based on common features.
“Yanagimoto and Sugimoto [145] used a CNN on raw 16-channel EEG data to differentiate between positive and negative affective states, which is shown to outperform shallow models based on common features.”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [145]
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BackgroundDEAP: A Database for Emotion Analysis ;Using Physiological Signals
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Deep learning for electroencephalogram (EEG) classification tasks: a review
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- Authors
- Yanagimoto, M., & Sugimoto, C.
- Affiliations
- Yokohama National University (JP)
- Venue
- 2016 IEEE 9th International Workshop on Computational Intelligence and Applications (IWCIA)
- Publisher
- IEEE
- Published
- 2016-11
- Pages
- 27-32
- OpenAlex
- W2570697713
- Cited by
- 43
- References
- 16
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 7 · 2022: 4 · 2021: 7 · 2020: 6 · 2019: 4 · 2018: 5 · 2017: 4
- FWCI
- 5.01
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Workshop on Computational Intelligence and Applications (IWCIA) (publishedVersion)
- Funders
- Japan Society for the Promotion of Science
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- EEG-based emotion recognition, emotional valence, convolutional neural networks, DEAP dataset, training data size, cross-subject generalization, brain-computer interface
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