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Objective. Electroencephalography (EEG) has been widely used in motor imagery (MI) research by virtue of its high temporal resolution and low cost, but its low spatial resolution is still a major criticism. The EEG source localization (ESL) algorithm effectively improves the spatial resolution of the signal by inverting the scalp EEG to extrapolate the cortical source signal, thus enhancing the classification accuracy. Approach. To address the problem of poor spatial resolution of EEG signals, this paper proposed a sub-band source chaotic entropy feature extraction method based on sub-band ESL. Firstly, the preprocessed EEG signals were filtered into 8 sub-bands. Each sub-band signal was source localized respectively to reveal the activation patterns of specific frequency bands of the EEG signals and the activities of specific brain regions in the MI task. Then, approximate entropy, fuzzy entropy and permutation entropy were extracted from the source signal as features to quantify the complexity and randomness of the signal. Finally, the classification of different MI tasks was achieved using support vector machine. Main result. The proposed method was validated on two MI public datasets (brain-computer interface (BCI) competition III IVa, BCI competition IV 2a) and the results showed that the classification accuracies were higher than the existing methods. Significance. The spatial resolution of the signal was improved by sub-band EEG localization in the paper, which provided a new idea for EEG MI research., (© 2024 IOP Publishing Ltd.)

Recent researches on emotion recognition suggests that domain adaptation, a form of transfer learning, has the capability to solve the cross-subject problem in Affective brain-computer interface (aBCI) field. However, traditional domain adaptation methods perform single to single domain transfer or simply merge different source domains into a larger domain to realize the transfer of knowledge, resulting in negative transfer. In this study, a multi-source transfer learning framework was proposed to promote the performance of multi-source electroencephalogram (EEG) emotion recognition. The method first used the data distribution similarity ranking (DDSA) method to select the appropriate source domain for each target domain off-line, and reduced data drift between domains through manifold feature mapping on Grassmann manifold. Meanwhile, the minimum redundancy maximum correlation algorithm (mRMR) was employed to select more representative manifold features and minimized the conditional distribution and marginal distribution of the manifold features, and then learned the domain-invariant classifier by summarizing structural risk minimization (SRM). Finally, the weighted fusion criterion was applied to further improve recognition performance. We compared our method with several state-of-the-art domain adaptation techniques using the SEED and DEAP dataset. Results showed that, compared with the conventional MEDA algorithm, the recognition accuracy of our proposed algorithm on SEED and DEAP dataset were improved by 6.74% and 5.34%, respectively. Besides, compared with TCA, JDA, and other state-of-the-art algorithms, the performance of our proposed method was also improved with the best average accuracy of 86.59% on SEED and 64.40% on DEAP. Our results demonstrated that the proposed multi-source transfer learning framework is more effective and feasible than other state-of-the-art methods in recognizing different emotions by solving the cross-subject problem., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interests regarding the publication of this article., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The non-stationary nature of electroencephalography (EEG) signals and individual variability makes it challenging to obtain EEG signals from users by utilizing brain-computer interface techniques. Most of the existing transfer learning methods are based on batch learning in offline mode, which cannot adapt well to the changes generated by EEG signals in the online situation. To address this problem, a multi-source online migrating EEG classification algorithm based on source domain selection is proposed in this paper. By utilizing a small number of labeled samples from the target domain, the source domain selection method selects the source domain data similar to the target data from multiple source domains. After training a classifier for each source domain, the proposed method adjusts the weight coefficients of each classifier according to the prediction results to avoid the negative transfer problem. This algorithm was applied to two publicly available motor imagery EEG datasets, namely, BCI Competition Ⅳ Dataset Ⅱa and BNCI Horizon 2020 Dataset 2, and it achieved average accuracies of 79.29 and 70.86%, respectively, which are superior to those of several multi-source online transfer algorithms, confirming the effectiveness of the proposed algorithm.

Background: Brain computer interface (BCI) utilizes brain signals to help users interact with external devices directly. EEG is one of the most commonly used techniques for brain signal acquisition in BCI. However, it is notoriously difficult to build a generic EEG recognition model due to significant non-stationarity and subject-to-subject variations, and the requirement for long time training. Transfer learning (TL) is particularly useful because it can alleviate the calibration requirement in EEG-based BCI applications by transferring the calibration information from existing subjects to new subject. To take advantage of geometric properties in Riemann manifold and joint distribution adaptation, a manifold embedded transfer learning (METL) framework was proposed for motor imagery (MI) EEG decoding., New Method: First, the covariance matrices of the EEG trials are first aligned on the SPD manifold. Then the features are extracted from both the symmetric positive definite (SPD) manifold and Grassmann manifold. Finally, the classification model is learned by combining the structural risk minimization (SRM) of source domain and joint distribution alignment of source and target domains., Result: Experimental results on two MI EEG datasets verify the effectiveness of the proposed METL. In particular, when there are a small amount of labeled samples in the target domain, METL demonstrated a more accurate and stable classification performance than conventional methods., Comparison With Existing Methods: Compared with several state-of-the-art methods, METL has achieved better classification accuracy, 71.81% and 69.06% in single-to-single (STS), 83.14% and 76.00% in multi-to-single (MTS) transfer tasks, respectively., Conclusions: METL can cope with single source domain or multi-source domains and compared with single-source transfer learning, multi-source transfer learning can improve the performance effectively due to the data expansion. It is effective enough to achieve superior performance for classification of EEG signals., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Hybrid EEG-fNIRS brain-computer interface (HBCI) is widely employed to enhance BCI performance. EEG and fNIRS signals are combined to increase the dimensionality of the information. Time windows are used to select EEG and fNIRS singles synchronously. However, it ignores that specific modal signals have their own characteristics, when the task is stimulated, the information between the modalities will mismatch at the moment, which has a significant impact on the classification performance. Here we propose a novel crossing time windows optimization for mental arithmetic (MA) based BCI. The EEG and fNIRS signals were segmented separately by sliding time windows. Then crossing time windows (CTW) were combined with each one segment from EEG and fNIRS selected independently. Furthermore, EEG and fNIRS features were extracted using Filter Bank Common Spatial Pattern (FBCSP) and statistical methods from each sample. Mutual information was calculated for FBCSP and statistical features to characterize the discrimination of crossing time windows, and the optimal window would be selected based on the largest mutual information. Finally, a sparse structured framework of Fisher Lasso feature selection (FLFS) was designed to select the joint features, and conventional Linear Discriminant Analysis (LDA) was employed to perform classification. We used proposed method for a MA dataset. The classification accuracy of the proposed method is 92.52 ± 5.38% and higher than other methods, which shows the rationality and superiority of the proposed method.

Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt's estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

Electroencephalography (EEG) signal classification is a crucial part in motor imagery brain–computer interface (BCI) system. Traditional supervised learning methods have performed well pleasing in EEG classification. Unfortunately, the unlabeled samples are easier to collect than labeled samples. In addition, recent studies have shown that it may degenerate performance of semi-supervised learning by exploiting unlabeled samples without selection. To address these issues, a novel semi-supervised broad learning system with transfer learning (TSS-BLS) is proposed in this paper. First, the pseudo-labels of unlabeled samples are obtained using the joint distribution adaptation algorithm. TSS-BLS is then constructed by an improved manifold regularization framework containing both labeled and pseudo-label information. Finally, the effectiveness of the proposed TSS-BLS is evaluated on three BCI competition datasets and four benchmark datasets from UCI repository and compared with seven state-of-the-art algorithms, including ELM, SS-ELM, HELM, SVM, LapSVM, BLS and GSS-BLS. Experimental results show that the performance of TSS-BLS is superior to BLS and GSS-BLS on average. It is thereby shown that TSS-BLS is safe and efficient for EEG classification. [ABSTRACT FROM AUTHOR]

The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic accidents were caused by driving fatigue. EEG is considered as a direct, effective, and promising modality to detect driving fatigue. In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection. EEG signals were recorded from six healthy volunteers in a simulated driving experiment. The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet). In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection. Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field. We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue. This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection. [ABSTRACT FROM AUTHOR]

Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most of them cannot extract sufficient significant information which leads to a less efficient classification. In this paper, we propose a novel approach called FDDL-ELM, which combines the discriminative power of extreme learning machine (ELM) with the reconstruction capability of sparse representation. Firstly, the common spatial pattern (CSP) algorithm is adopted to perform spatial filtering on raw EEG data to enhance the task-related neural activity. Secondly, the Fisher discrimination criterion is employed to learn a structured dictionary and obtain sparse coding coefficients from the filtered data, and these discriminative coefficients are then used to acquire the reconstructed feature representations. Finally, a nonlinear classifier ELM is used to identify these features in different MI tasks. The proposed method is evaluated on 2-class Datasets IVa and IIIa of BCI Competition III and 4-class Dataset IIa of BCI Competition IV. Experimental results show that our method achieved superior performance than the other existing algorithms and yielded the accuracies of 80.68%, 87.54%, and 63.76% across all subjects in the above-mentioned three datasets, respectively. [ABSTRACT FROM AUTHOR]

Driving fatigue accounts for a large number of traffic accidents in modern life nowadays. It is therefore of great importance to reduce this risky factor by detecting the driver's drowsiness condition. This study aimed to detect drivers' drowsiness using an advanced electroencephalography (EEG)-based classification technique. We first collected EEG data from six healthy adults under two different awareness conditions (wakefulness and drowsiness) in a virtual driving experiment. Five different machine learning techniques, including the K-nearest neighbor (KNN), support vector machine (SVM), extreme learning machine (ELM), hierarchical extreme learning machine (H-ELM), and the proposed modified hierarchical extreme learning machine algorithm with particle swarm optimization (PSO-H-ELM), were applied to classify the subject's drowsiness based on the power spectral density (PSD) feature extracted from the EEG data. The mean accuracies of the five classifiers were 79.31%, 79.31%, 74.08%, 81.67%, and 83.12%, respectively, demonstrating the superior performance of our new PSO-H-ELM algorithm in detecting drivers' drowsiness, compared to the other techniques. [ABSTRACT FROM AUTHOR]

In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signals have gotten satisfactory results. However, unlabeled samples are more frequent than labeled samples, so how to simultaneously utilize limited labeled samples and many unlabeled samples becomes a research hotspot. In this paper, we propose a new graph-based semi-supervised broad learning system (GSS-BLS), which combines the graph label propagation method to obtain pseudo-labels and then trains the GSS-BLS classifier together with other labeled samples. Three BCI competition datasets are used to assess the GSS-BLS approach and five comparison algorithms: BLS, ELM, HELM, LapSVM and SMIR. The experimental results show that GSS-BLS achieves satisfying Cohen's kappa values in three datasets. GSS-BLS achieves the better results of each subject in the 2-class and 4-class datasets and has significant improvements compared with original BLS except subject C6. Therefore, the proposed GSS-BLS is an effective semi-supervised algorithm for classifying EEG signals. [ABSTRACT FROM AUTHOR]