Discriminative feature selection is vital for enhancing motor imagery decoding performance in electroencephalogram (EEG) signals. However, existing feature optimization methods have not sufficiently explored the intrinsic attribute distribution of features and their associations with the target class, which could result in spurious correlations between optimized features and class labels, yielding suboptimal performance. Therefore, this study proposed an iterative Weighted Sparse-Group Lasso (iWSGL) model for optimizing Common Spatial Pattern (CSP)-based high-dimensional features, thus further enhancing the decoding accuracy of motor imagery. Specifically, the affinity propagation (AP) clustering algorithm was utilized to adaptively partition the high-dimensional features into multiple groups based on the underlying relationships among them. To evaluate the significance of individual feature within each group and the overall significance of the groups themselves, a weight calculation method was proposed based on conditional entropy. With the weights and feature structural information, a weighted sparse regression model was devised within the iterative Sparse-Group Lasso (iSGL) framework to jointly optimize the CSP-based high-dimensional features. The performance of the proposed method was validated on three datasets using the support vector machine (SVM). The experimental results exhibited the exceptional superiority of the proposed method over the current CSP and its variants, demonstrating its remarkable performance. These findings imply that the proposed model can offer a novel optimization strategy for enhancing pattern recognition of brain intentions in Brain-Computer Interface (BCI) applications. 

Improvement of Motor Imagery Electroencephalogram Decoding by Iterative Weighted Sparse-Group Lasso

The global rise in lower limb amputation cases necessitates advancements in prosthetic limb technology to enhance the quality of life for affected patients. This review paper explores recent advancements in the integration of EEG and fNIRS modalities for smart lower prosthetic limbs for rehabilitation applications. The paper synthesizes current research progress, focusing on the synergy between brain-computer interfaces and neuroimaging technologies to enhance the functionality and user experience of lower limb prosthetics. The review discusses the potential of EEG and fNIRS in decoding neural signals, enabling more intuitive and responsive control of prosthetic devices. Additionally, the paper highlights the challenges, innovations, and prospects associated with the incorporation of these neurotechnologies in the field of rehabilitation. The insights provided in this review contribute to a deeper understanding of the evolving landscape of smart lower prosthetic limbs and pave the way for more effective and user-friendly solutions in the realm of neurorehabilitation. 

Recent progress on smart lower prosthetic limbs: a comprehensive review on using EEG and fNIRS devices in rehabilitation

Brain-computer interfaces (BCI) based motor imagery (MI) has become a research hotspot for establishing a flexible communication channel for patients with apoplexy or degenerative pathologies. Accurate decoding of motor imagery electroencephalography (MI-EEG) signals, while essential for effective BCI systems, is still challenging due to the significant noise inherent in the EEG signals and the lack of informative correlation between the signals and brain activities. The application of deep learning for EEG feature representation has been rarely investigated, nevertheless bringing improvements to the performance of motor imagery classification. This paper proposes a deep learning decoding method based on multi-hierarchical representation fusion (MHRF) on MI-EEG. It consists of a concurrent framework constructed of bidirectional LSTM (Bi-LSTM) and convolutional neural network (CNN) to fully capture the contextual correlations of MI-EEG and the spectral feature. Also, the stacked sparse autoencoder (SSAE) is employed to concentrate these two domain features into a high-level representation for cross-session and subject training guidance. The experimental analysis demonstrated the efficacy and practicality of the proposed approach using a public dataset from BCI competition IV and a private one collected by our MI task. The proposed approach can serve as a robust and competitive method to improve inter-session and inter-subject transferability, adding anticipation and prospective thoughts to the practical implementation of a calibration-free BCI system.

/pdf/multi-hierarchical-fusion-to-capture-the-latent-invariance-3p78mqyk.pdf

Multi-Hierarchical Fusion to Capture the Latent Invariance for Calibration-Free Brain-Computer Interfaces

To address the fact that the classical motor imagination paradigm has no noticeable effect on the rehabilitation training of upper limbs in patients after stroke and the corresponding feature extraction algorithm is limited to a single domain, this paper describes the design of a unilateral upper-limb fine motor imagination paradigm and the collection of data from 20 healthy people. It presents a feature extraction algorithm for multi-domain fusion and compares the common spatial pattern (CSP), improved multiscale permutation entropy (IMPE) and multi-domain fusion features of all participants through the use of decision tree, linear discriminant analysis, naive Bayes, a support vector machine, k-nearest neighbor and ensemble classification precision algorithms in the ensemble classifier. For the same subject, the average classification accuracy improvement of the same classifier for multi-domain feature extraction relative to CSP feature results went up by 1.52%. The average classification accuracy improvement of the same classifier went up by 32.87% relative to the IMPE feature classification results. This study's unilateral fine motor imagery paradigm and multi-domain feature fusion algorithm provide new ideas for upper limb rehabilitation after stroke.

A multi-feature fusion decoding study for unilateral upper-limb fine motor imagery.

Brain-computer interfaces (BCI) enable direct communication with external equipment, using neural activity as the control signal. Electroencephalogram (EEG) signals are usually selected as the control signal. For EEG signals obtained from a given experimental paradigm, a superior algorithm for feature extraction and classification is very significant. As one of the representative algorithms of deep learning, the convolutional neural network (CNN) has been widely used in the field of BCI. In this work, we introduce the filter-bank convolutional network (FBCNet) and propose an improved method. It mainly improves the network performance by modifying the loss function. The single loss function in the network is improved to the multi-loss fusion functions. Various loss functions are added to the network, and the characteristics of different loss functions are used to train the network to improve the network classification performance. This method of multi-loss fusion functions is validated on a dataset of 11 healthy subjects and compared with the other three benchmark algorithms. The result shows that the improved FBCNet produces a four-classes accuracy of 78.5%, which is superior to other algorithms.

Motor imagery EEG decoding based on multi-loss fusion FBCNet

Multi-feature fusion method based on WOSF and MSE for four-class MI EEG identification

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Multi-feature fusion method based on WOSF and MSE for four-class MI EEG identification