EEG Feature Selection via Global Redundancy Minimization for Emotion Recognition

Xu, Xueyuan; Jia, Tianyuan; Li, Qing; Wei, Fulin; Ye, Long; Wu, Xia

doi:10.1109/taffc.2021.3068496

Cited by 21 publications

(9 citation statements)

References 78 publications

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“…We conduct extensive experiments to evaluate the validity and reliability of the proposed EFSMDER method on three EEG emotional databases, including DREAMER [21], DEAP [22], and HDED [13]. All the databases adopt the multidimension emotion model to represent human emotions, i.e.…”

Section: A Database Descriptionmentioning

confidence: 99%

“…Nevertheless, the associated EEG features are often high-dimensional and inevitably contain irrelevant, redundant, and noise information, which can easily deteriorate the emotion recognition performance due to the relatively small amount of EEG samples [9], [10]. To select informative features and remove irrelevant features from the high-dimensional EEG data, multiple feature selection approaches have been implemented in the EEG-based emotion recognition task [11]- [13].…”

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confidence: 99%

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Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition via Local and Global Label Relevance

Xu,

Wei,

Jia

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Due to the problem of a small amount of EEG samples and relatively high dimensionality of electroencephalogram (EEG) features, feature selection plays an essential role in EEG-based emotion recognition. However, current EEG-based emotion recognition studies utilize a problem transformation approach to transform multidimension emotional labels into single-dimension labels, and then implement commonly used single-label feature selection methods to search feature subsets, which ignores the relations between different emotional dimensions. To tackle the problem, we propose an efficient EEG feature selection method for multi-dimension emotion recognition (EFSMDER) via local and global label relevance. First, to capture the local label correlations, EFSMDER implements orthogonal regression to map the original EEG feature space into a low-dimension space. Then, it employs the global label correlations in the original multi-dimension emotional label space to effectively construct the label information in the low-dimension space. With the aid of local and global relevance information, EFSMDER can conduct representational EEG feature subset selection. Three EEG emotional databases with multi-dimension emotional labels were used for performance comparison between EFSMDER and fourteen state-of-the-art methods, and the EFSMDER method achieves the best multi-dimension classification accuracies of 86.43, 84.80, and 97.86 percent on the DREAMER, DEAP, and HDED datasets, respectively.

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Section: A Database Descriptionmentioning

confidence: 99%

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confidence: 99%

Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition via Local and Global Label Relevance

Xu,

Wei,

Jia

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Literature [16] puts forward that in college physical education, emotional educators enthusiastically carry out educational activities, induce and stimulate the positive emotion and positive attitude of the educatees in the process of education, make them in the best state of psychology, and regard the emotional cultivation of the educatees as one of the objectives of education. In literature [17] through the big data analysis method, in college physical education teaching, if physical education teachers can grasp students' inner feelings and use their own cultivation to adjust students' psychological trends and inner needs, they will shorten the psychological distance with students, produce a good classroom atmosphere, and achieve the desired teaching effect.…”

Section: Related Workmentioning

confidence: 99%

Analysis on the Penetration of Emotional Education in College Physical Education Based on Emotional Feature Clustering

Guo

Wang

2022

Scientific Programming

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Physical education is a highly skilled education offered in colleges and universities. Teachers do not appear in front of inanimate machines as laborers, and they are not the same as gardeners who grow colorful trees, according to their essential characteristics. Their work is aimed at flesh-and-blood students who are sentimental, thoughtful, and engaged in critical thinking. As a result, schools should prioritize physical education and place a premium on emotional infiltration education, improve learning interest, improve teacher-student relationships, create a harmonious teaching environment, and improve teaching quality; it has a significant impact on an individual’s entire life. The modern educational process places a premium on the transmission of rational knowledge while overlooking the accumulation of emotional experience. The cultivation and development of emotional feeling ability, emotional expression, and expression ability receive less attention than the training and improvement of language, concept, logic, and reasoning abilities. Emotion feature clustering is used to propose an emotion recognition method in this study. This method generates extended features for classification by constructing a co-occurrence matrix based on the co-occurrence relationship of emotion features and then by applying the spectral clustering method. The binary value of whether emotional features of emotional education in college physical education appear in a particular cluster is then expressed as a feature and extended to the original training feature set, alleviating the problem of sparse features.

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“…TJM jointly performs two TL methods, instance reweighting and feature matching alignment, in a principled dimensionality reduction procedure. It's suitable for EEG data with noises and highdimensional features [27], [28]. However, it works only in a single source to the target, so we extend it to a multiple sources condition.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Source Transfer Joint Matching Method for Inter-Subject Motor Imagery Decoding

Wei

Jia

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Individual differences among different subjects pose a great challenge to motor imagery (MI) decoding. Multi-source transfer learning (MSTL) is one of the most promising ways to reduce individual differences, which can utilize rich information and align the data distribution among different subjects. However, most MSTL methods in MI-BCI combine all data in the source subjects into a single mixed domain, which will ignore the effect of important samples and the large differences in multiple source subjects. To address these issues, we introduce transfer joint matching and improve it to multi-source transfer joint matching (MSTJM) and weighted MSTJM (wM-STJM). Different from previous MSTL methods in MI, our methods align the data distribution for each pair of subjects, and then integrate the results by decision fusion. Besides that, we design an inter-subject MI decoding framework to verify the effectiveness of these two MSTL algorithms. It mainly consists of three modules: covariance matrix centroid alignment in the Riemannian space, source selection in the Euclidean space after tangent space mapping to reduce negative transfer and computation overhead, and further distribution alignment by MSTJM or wMSTJM. The superiority of this framework is verified on two common public MI datasets from BCI competition IV. The average classification accuracy of the MSTJM and wMSTJ methods outperformed other state-of-the-art methods by at least 4.24% and 2.62% respectively. It's promising to advance the practical applications of MI-BCI.

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EEG Feature Selection via Global Redundancy Minimization for Emotion Recognition

Cited by 21 publications

References 78 publications

Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition via Local and Global Label Relevance

Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition via Local and Global Label Relevance

Analysis on the Penetration of Emotional Education in College Physical Education Based on Emotional Feature Clustering

A Multi-Source Transfer Joint Matching Method for Inter-Subject Motor Imagery Decoding

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