Due to the intracranial volume conduction effects, high-dimensional multi-channel electroencephalography (EEG) features often contain substantial redundant and irrelevant information. This issue not only hinders the extraction of discriminative emotional representations but also compromises real-time performance. Feature selection has been established as an effective approach to address the challenges while enhancing the transparency and interpretability of emotion recognition models. However, existing EEG feature selection research overlooks the influence of latent EEG feature structures on emotional label correlations and assumes uniform importance across various channels, directly limiting the precise construction of EEG feature selection models for multi-dimensional affective computing. To overcome these issues, this paper proposes a channel-wise EEG feature selection (CWEFS) method for multi-dimensional emotion recognition. Inspired by the volume conduction effects, CWEFS models feature selection within a shared latent structure that captures a consensus representation across EEG channels. This consensus space is jointly learned with a latent semantic analysis of emotional labels to preserve local geometric structure. Furthermore, CWEFS incorporates adaptive channel-weight learning to automatically assess the contribution of each channel. Comprehensive experimental results, compared against nineteen popular feature selection methods, demonstrate that the EEG feature subsets chosen by CWEFS achieve optimal emotion recognition performance across six evaluation metrics.

Due to the small size of valid samples, multi-source EEG features with high dimensionality can easily cause problems such as overfitting and poor real-time performance of the emotion recognition classifier. Feature selection has been demonstrated as an effective means to solve these problems. Current EEG feature selection research assumes that all dimensions of emotional labels are complete. However, owing to the open acquisition environment, subjective variability, and border ambiguity of individual perceptions of emotion, the training data in the practical application often includes missing information, i.e., multi-dimensional emotional labels of several instances are incomplete. The aforementioned incomplete information directly restricts the accurate construction of the EEG feature selection model for multi-dimensional emotion recognition. To wrestle with the aforementioned problem, we propose a novel EEG feature selection model with weighted self-expression learning (WSEL). The model utilizes self-representation learning and least squares regression to reconstruct the label space through the second-order correlation and higher-order correlation within the multi-dimensional emotional labels and simultaneously realize the EEG feature subset selection under the incomplete information. We have utilized two multimedia-induced emotion datasets with EEG recordings, DREAMER and DEAP, to confirm the effectiveness of WSEL in the missing multi-dimensional emotional feature selection challenge. Compared to nine state-of-the-art feature selection approaches, the experimental results demonstrate that the EEG feature subsets chosen by WSEL can achieve optimal performance in terms of six performance metrics.

A major challenge in motor imagery Brain-Computer Interfaces (MI-BCIs) arises from domain shift due to large individual differences. Currently, most cross-subject MI-BCI decoding methods rely on transfer learning to extract subject-shared features or align data distributions. However, these methods typically require all unlabeled data from the target subjects or labeled calibration data, which is unavailable in practical applications. To address this, we propose a brain lateralization-guided subject adaptive network, BLSAN, to enhance model generalization through local-global adversarial training. Specifically, two separate adversarial networks for left and right hemispheres are designed to reduce local differences, and features extracted from both hemispheres are combined for global adversarial training. Additionally, we design a confidence-based pseudo label generation method to enhance model discriminability. We validate the effectiveness of our approach on two public MI datasets, BCI Competition IV 2a and 2b, only with some unlabeled calibration data, which improves the practicality of MI-BCIs.

Although advances in deep learning technologies have greatly facilitated the brain intention decoding from electroencephalogram (EEG) in motor imagery brain–computer interfaces (MI-BCIs), significant individual differences hinder the practical cross-subject MI-BCI applications. Unlike other existing domain adversarial transfer networks that focus on designing different discriminators to reduce individual differences, inspired by the motor lateralization phenomenon, we innovatively utilize transformer and the spatiotemporal pattern differences of EEG as prior knowledge to enhance the feature discriminability in our brain decoding adversarial network. In addition, to address adversarial network decision boundaries bias toward the source domain, we propose a data augmentation method, EEGMix to rapidly mix and enrich the target domain data. With an adaptive adversarial factor, our decoding model reduces the differences in marginal and conditional distribution simultaneously. Three public MI datasets, 2a, 2b, and OpenBMI verified our model's effectiveness. The accuracy achieved 77.49%, 85.19%, and 79.37%, superior to other state-of-the-art algorithms.

A common drawback of EEG-based emotion recognition is that volume conduction effects of the human head introduce interchannel dependence and result in highly correlated information among most EEG features. These highly correlated EEG features cannot provide extra useful information, and they actually reduce the performance of emotion recognition. However, the existing feature selection methods, commonly used to remove redundant EEG features for emotion recognition, ignore the correlation between the EEG features or utilize a greedy strategy to evaluate the interdependence, which leads to the algorithms retaining the correlated and redundant features with similar feature scores in the EEG feature subset. To solve this problem, we propose a novel EEG feature selection method for emotion recognition, termed global redundancy minimization in orthogonal regression (GRMOR). GRMOR can effectively evaluate the dependence among all EEG features from a global view and then select a discriminative and nonredundant EEG feature subset for emotion recognition. To verify the performance of GRMOR, we utilized three EEG emotional data sets (DEAP, SEED, and HDED) with different numbers of channels (32, 62, and 128). The experimental results demonstrate that GRMOR is a promising tool for redundant feature removal and informative feature selection from highly correlated EEG features.

Anxiety is a complex emotional state that has a great impact on people's physical and mental health. Effectively identifying different anxiety states is very important. By inducing various anxiety states of 12 healthy college students with electroencephalograph (EEG) recording, comprehensive EEG features, including not only commonly used frequency domain features but also the time domain, statistical and nonlinear features were extracted from different EEG bands and brain locations. Next, correlation analysis was performed between various features and anxiety level changes that were predetermined at each stage of the experiment using a 5-point Likert scale, and the most relevant features were collected. Then, different classifiers were applied to classify four anxiety levels using different features alone or together to explore their anxiety recognition ability. Based on our dataset, the highest accuracy of identifying four anxiety states reached approximately 62.56 percent using the Support Vector Machine (SVM), which improved the classification accuracy compared with previous studies. The results also revealed the importance of EEG linear features (especially for features including total power, mean square and variance) in anxiety recognition. Furthermore, it suggested that EEG features in the beta band and the frontal lobe contributed to anxiety recognition more than the features in the other bands or other brain locations. In short, this study improves the accuracy of multi-level anxiety recognition and helps in choosing better features for anxiety recognition, which lay the foundation for the detection of continuous anxiety changes.

Feature selection has attracted a lot of attention in obtaining discriminative and non-redundant features from high-dimension data. Compared with traditional filter and wrapper methods, embedded methods can obtain a more informative feature subset by fully considering the importance of features in the classification tasks. However, the existing embedded methods emphasize the above importance of features and mostly ignore the correlation between the features, which leads to retain the correlated and redundant features with similar scores in the feature subset. To solve the problem, we propose a novel supervised embedded feature selection framework, called feature selection under global redundancy minimization in orthogonal regression (GRMOR). The proposed framework can effectively recognize redundant features from a global view of redundancy among the features. We also incorporate the large margin constraint into GRMOR for robust multi-class classification. Compared with the traditional embedded methods based on least square regression, the proposed framework utilizes orthogonal regression to preserve more discriminative information in the subspace, which can help accurately rank the importance of features in the classification tasks. Experimental results on twelve public datasets demonstrate that the proposed framework can obtain superior classification performance and redundancy removal performance than twelve other feature selection methods.

Effective features can improve the performance of a model and help us understand the characteristics and underlying structure of complex data. Previously proposed feature selection methods usually cannot retain more discriminative information. To address this shortcoming, we propose a novel supervised orthogonal least square regression model with feature weighting for feature selection. The optimization problem of the objective function can be solved by employing generalized power iteration and augmented Lagrangian multiplier methods. Experimental results show that the proposed method can more effectively reduce feature dimensionality and obtain better classification results than traditional feature selection methods. The convergence of our iterative method is also proved. Consequently, the effectiveness and superiority of the proposed method are verified both theoretically and experimentally.

Various supervised embedded methods have been proposed to select discriminative features from original ones, such as Feature Selection with Orthogonal Regression (FSOR) and Robust Feature Selection. Compared with embedded methods based on the least square regression, FSOR, utilizing orthogonal regression, can preserve more discriminative information in the subspace and have better performance on feature selection. However, the embedded approaches have scarcely considered the dependency among the selected feature subset. To address the defect, in this paper, we propose a two-stage (filter-embedded) feature selection technique based on Maximum Relevance Minimum Redundancy and FSOR, termed as Orthogonal Regression with Minimum Redundancy (ORMR). We compared the feature selection performance between ORMR and nine other state-of-the-art supervised feature selection methods on six benchmark datasets. The results demonstrate the advantage of ORMR method over others in choosing discriminative features with considering the redundant information among the selected feature subset.

A common drawback of the EEG applications is that the volume conduction of human head leads to lots of redundant information in EEG recordings. To reduce the redundancy and choose informative EEG features, in this paper, we propose an EEG feature selection technique, termed as Feature Selection with Orthogonal Regression (FSOR). Compared with classical feature selection methods, for nonlinear and nonstationary EEG signals, FSOR can employ orthogonal regression to preserve more discriminative information in the subspace. To verify the EEG feature selection performance, we collected a multichannel EEG dataset for emotion recognition and compared FSOR with two popular feature selection methods. The experimental results demonstrate the advantage of FSOR method over others for reducing the redundant information among the EEG relevant features. Additionally, we found that the absolute power ratio of beta wave to theta wave is the most discriminative feature, and beta band is the critical band for emotion recognition.