Emotion recognition using time–frequency ridges of EEG signals based on multivariate synchrosqueezing transform
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Ahmet Mert
und
Hasan Huseyin Celik
Abstract
The feasibility of using time–frequency (TF) ridges estimation is investigated on multi-channel electroencephalogram (EEG) signals for emotional recognition. Without decreasing accuracy rate of the valence/arousal recognition, the informative component extraction with low computational cost will be examined using multivariate ridge estimation. The advanced TF representation technique called multivariate synchrosqueezing transform (MSST) is used to obtain well-localized components of multi-channel EEG signals. Maximum-energy components in the 2D TF distribution are determined using TF-ridges estimation to extract instantaneous frequency and instantaneous amplitude, respectively. The statistical values of the estimated ridges are used as a feature vector to the inputs of machine learning algorithms. Thus, component information in multi-channel EEG signals can be captured and compressed into low dimensional space for emotion recognition. Mean and variance values of the five maximum-energy ridges in the MSST based TF distribution are adopted as feature vector. Properties of five TF-ridges in frequency and energy plane (e.g., mean frequency, frequency deviation, mean energy, and energy deviation over time) are computed to obtain 20-dimensional feature space. The proposed method is performed on the DEAP emotional EEG recordings for benchmarking, and the recognition rates are yielded up to 71.55, and 70.02% for high/low arousal, and high/low valence, respectively.
Research funding: None declared.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: The authors declare that they have no conflict of interest.
Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.
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© 2021 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Attention based convolutional network for automatic sleep stage classification
- Emotion recognition using time–frequency ridges of EEG signals based on multivariate synchrosqueezing transform
- A novel signal to image transformation and feature level fusion for multimodal emotion recognition
- PVC arrhythmia classification based on fractional order system modeling
- A clinical set-up for noninvasive blood pressure monitoring using two photoplethysmograms and based on convolutional neural networks
- Virtual simulation of otolith movement for the diagnosis and treatment of benign paroxysmal positional vertigo
- Development and control of a home-based training device for hand rehabilitation with a spring and cable driven mechanism
- An easy and low-cost biomagnetic methodology to study regional gastrointestinal transit in rats
- Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks
- Comparison of a standardized four-point bending test to an implant system test of an osteosynthetic system under static and dynamic load condition
- An application of finite element method in material selection for dental implant crowns
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Attention based convolutional network for automatic sleep stage classification
- Emotion recognition using time–frequency ridges of EEG signals based on multivariate synchrosqueezing transform
- A novel signal to image transformation and feature level fusion for multimodal emotion recognition
- PVC arrhythmia classification based on fractional order system modeling
- A clinical set-up for noninvasive blood pressure monitoring using two photoplethysmograms and based on convolutional neural networks
- Virtual simulation of otolith movement for the diagnosis and treatment of benign paroxysmal positional vertigo
- Development and control of a home-based training device for hand rehabilitation with a spring and cable driven mechanism
- An easy and low-cost biomagnetic methodology to study regional gastrointestinal transit in rats
- Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks
- Comparison of a standardized four-point bending test to an implant system test of an osteosynthetic system under static and dynamic load condition
- An application of finite element method in material selection for dental implant crowns
