Transfer learning for motor imagery-based brain-computer interfaces (MI-BCIs) struggles with inter-subject variability, hindering its generalization to new users. This paper proposes an advanced implicit transfer learning framework, META-EEG, designed to overcome the challenge arising from inter-subject variability. By incorporating gradient-based meta-learning with an intermittent freezing strategy, META-EEG ensures efficient feature representation learning, providing a robust zero-calibration solution. A comparative analysis reveals that META-EEG significantly outperforms all the baseline methods and competing methods on three different public datasets. Moreover, we demonstrate the efficiency of the proposed model through a neurophysiological and feature-representational analysis. With its robustness and superior performance on challenging datasets, META-EEG provides an effective solution for calibration-free MI-EEG classification, facilitating broader usability.

Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting millions of individuals worldwide. Functional Magnetic Resonance Imaging (fMRI) has emerged as a promising tool for the objective diagnosis of MDD, enabling the investigation of functional connectivity patterns in the brain associated with this disorder. However, most existing methods focus on a single brain atlas, which limits their ability to capture the complex, multi-scale nature of functional brain networks. To address these limitations, we propose a novel multi-atlas fusion method that incorporates early and late fusion in a unified framework. Our method introduces the concept of the holistic Functional Connectivity Network (FCN), which captures both intra-atlas relationships within individual atlases and inter-regional relationships between atlases with different brain parcellation scales. This comprehensive representation enables the identification of potential disease-related patterns associated with MDD in the early stage of our framework. Moreover, by decoding the holistic FCN from various perspectives through multiple spectral Graph Convolutional Neural Networks and fusing their results with decision-level ensembles, we further improve the performance of MDD diagnosis. Our approach is easily implemented with minimal modifications to existing model structures and demonstrates a robust performance across different baseline models. Our method, evaluated on public resting-state fMRI datasets, surpasses the current multi-atlas fusion methods, enhancing the accuracy of MDD diagnosis. The proposed novel multi-atlas fusion framework provides a more reliable MDD diagnostic technique. Experimental results show our approach outperforms both single- and multi-atlas-based methods, demonstrating its effectiveness in advancing MDD diagnosis.

Major Depressive Disorder (MDD) is a pervasive disorder affecting millions of individuals, presenting a significant global health concern. Functional connectivity (FC) derived from resting-state functional Magnetic Resonance Imaging (rs-fMRI) serves as a crucial tool in revealing functional connectivity patterns associated with MDD, playing an essential role in precise diagnosis. However, the limited data availability of FC poses challenges for robust MDD diagnosis. To tackle this, some studies have employed Deep Neural Networks (DNN) architectures to construct Generative Adversarial Networks (GAN) for synthetic FC generation, but this tends to overlook the inherent topology characteristics of FC. To overcome this challenge, we propose a novel Graph Convolutional Networks (GCN)-based Conditional GAN with Class-Aware Discriminator (GC-GAN). GC-GAN utilizes GCN in both the generator and discriminator to capture intricate FC patterns among brain regions, and the class-aware discriminator ensures the diversity and quality of the generated synthetic FC. Additionally, we introduce a topology refinement technique to enhance MDD diagnosis performance by optimizing the topology using the augmented FC dataset. Our framework was evaluated on publicly available rs-fMRI datasets, and the results demonstrate that GC-GAN outperforms existing methods. This indicates the superior potential of GCN in capturing intricate topology characteristics and generating high-fidelity synthetic FC, thus contributing to a more robust MDD diagnosis.

In recent years, deep learning methods have shown promising capabilities for extracting informative and discriminative features from electroencephalography (EEG) data. However, several studies have reported that the feature selection process followed by feature extraction can be beneficial to achieve further performance improvement. Even though a recent work achieved promising results by using the single-agent reinforcement learning (RL)-based framework to select task-relevant features in the temporal domain, it still failed to consider other significant features in the spatial–spectral domain. To overcome such limitations, we propose a cooperative multiagent RL-based framework (MARS) that performs feature selection in both the spatial–spectral and temporal domains simultaneously for a motor imagery (MI)-EEG classification task. In this framework, we enable our RL agents to collaborate with each other as a team to solve a complex multiobjective feature selection problem. Furthermore, we adopt a counterfactual advantage function to overcome the free-rider problem, which is associated with the credit assignment issue in multiagent cases. To assess the MARS framework, we conduct extensive experiments with two public MI datasets under subject-dependent and subject-independent scenarios and we apply the MARS to different backbone networks. The experimental results demonstrate that our MARS outperforms other competing methods in terms of mean accuracy and achieves statistically significant improvements.