Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition

Hao, Chao; Dong, Liang; Liu, Yongli; Lu, Bao-Yun

doi:10.1155/2020/6816502

Cited by 26 publications

(13 citation statements)

References 45 publications

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“…This is in line with our predictions that integrating the spatial configuration of the sensors in the 2D-IDR model as prior information to the classifier enhances its convergence and recognition performances. Furthermore, the superiority of 2D-IDR model was anticipated as shown in [45]. This work proposed a global feature learning method that encapsulates the multi-channel EEG signals into gray-level images.…”

Section: F Comparison With State-of-the-art Modelsmentioning

confidence: 99%

EEG Classification by Factoring in Sensor Spatial Configuration

et al. 2021

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Electroencephalography (EEG) serves as an effective diagnostic tool for mental disorders and neurological abnormalities. Enhanced analysis and classification of EEG signals can help improve performance in classifying the disorders and abnormalities. A new approach is examined here for enhancing EEG classification performance using a novel model of data representation that leverages knowledge of spatial layout of EEG sensors. An investigation of the performance of the proposed data representation model provides evidence of consistently higher classification accuracy of the proposed model compared with a model that ignores the sensor layout. The performance is assessed for models that represent the information content of the EEG signals in two different ways: a one-dimensional concatenation of the channels of the frequency bands and a proposed image-like two-dimensional representation of the EEG channel locations. The models are used in conjunction with different machine learning techniques. Performance of these models is examined on two tasks: social anxiety disorder classification, and emotion recognition using a dataset, DEAP, for emotion analysis using physiological signals. We hypothesize that the proposed two-dimensional model will significantly outperform the one-dimensional model and this is validated in our results as this model consistently yields 5-8% higher accuracy in all machine learning algorithms investigated. Among the algorithms investigated, Convolutional Neural Networks provide the best performance, far exceeding that of Support Vector Machine and k-Nearest Neighbors algorithms.

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Section: F Comparison With State-of-the-art Modelsmentioning

confidence: 99%

EEG Classification by Factoring in Sensor Spatial Configuration

et al. 2021

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“…It is important to determine the appropriate method to represent the features of the EEG signals due to their spatial information characteristics. Some of the representation methods used in previous studies include the multiband feature matrix (MFM) [62], 2D mesh [69], maximal information coefficient (MIC) [70], and 3D cube [40]. The 3D Cube method can maintain spatial information between channels as well as frequency bands, including theta, alpha, beta, and gamma.…”

Section: Feature Representationmentioning

confidence: 99%

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Wirawan

Wardoyo

Lelono

2022

IJECE

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Electroencephalogram (EEG) signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by: i) the distribution of the data used, ii) consider of differences in participant characteristics, and iii) consider the characteristics of the EEG signals. In response to these issues, this study will examine three important points that affect the success of emotion recognition packaged in several research questions: i) What factors need to be considered to generate and distribute EEG data?, ii) How can EEG signals be generated with consideration of differences in participant characteristics?, and iii) How do EEG signals with characteristics exist among its features for emotion recognition? The results, therefore, indicate some important challenges to be studied further in EEG signals-based emotion recognition research. These include i) determine robust methods for imbalanced EEG signals data, ii) determine the appropriate smoothing method to eliminate disturbances on the baseline signals, iii) determine the best baseline reduction methods to reduce the differences in the characteristics of the participants on the EEG signals, iv) determine the robust architecture of the capsule network method to overcome the loss of knowledge information and apply it in more diverse data set.

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“…After particles start from a random position, the velocities and positions of particles are updated according to the particle's best pBest and gBest at each iteration until the number of maximum iteration. The velocity of each particle is updated using equation (18).…”

Section: Swarm-intelligence Based Feature Selectionmentioning

confidence: 99%

“…Feature extraction, feature selection, and classification are the main stages, and the success of each step affects the performance of the entire system. The main issue is to find emotional salient features from several sources, analyzing feature sets [16], [17] to eliminate the irrelevant/unnecessary features and developing new classification frameworks to improve accuracies of existing classifiers [3], [18]. This study focuses on emotion recognition from EEG using band powers and phase-locking values as features and sophisticated feature selection method based on swarmintelligence (SI) algorithms and well-known classification algorithms such as k-nearest neighbour (k-NN), random forest, and support vector machines (SVM).…”

Section: Introductionmentioning

confidence: 99%

A Channel Selection Method for Emotion Recognition From EEG Based on Swarm-Intelligence Algorithms

2021

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Increasing demand for human-computer interaction applications has escalated the need for automatic emotion recognition as emotions are essential for natural communication. There are various information sources that can be used for recognizing emotions, such as speech, facial expressions, body movements, and physiological signals. Among those physiological signals are more reliable for better affective communication with machines since they are almost impossible to control. Therefore, automatic emotion recognition from EEG signals has been a topic intensely investigated. Emotions are experiences that arise various cognitive functions observed in different frequency bands involving multiple brain areas and recognition from EEG with high accuracies is only possible with a large number of features extracted from the whole brain in various bands. Emotion regulation also requires integration of cognitive functions and thus functional connectivity between regions should also be considered. In this paper, we extract 736 features based on spectral power and phase-locking values. We particularly focus on finding salient features for emotion recognition using swarm-intelligence (SI) algorithms. We applied well-known classification algorithms for recognizing positive and negative emotions using the feature sets that are selected by these algorithms. Besides, features that are selected by all of them commonly are used as a new feature set. We report accuracies between 56.27% and 60.29% on the average; noting that by decreasing the feature size by 87.17% (from 736 to 94.40) an average accuracy of 60.01±8.93 was obtained with the random forest classifier. We also highlight the efficient electrode locations for emotion recognition. As a result, we define 11 channels as dominant and promising classification results are obtained.

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Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition

Cited by 26 publications

References 45 publications

EEG Classification by Factoring in Sensor Spatial Configuration

EEG Classification by Factoring in Sensor Spatial Configuration

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

A Channel Selection Method for Emotion Recognition From EEG Based on Swarm-Intelligence Algorithms

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