Emotion recognition and consciousness detection are critical in neuroscience. Patients with disorder of consciousness (DOC) often cannot express feelings or respond definitively to stimuli due to impaired functions, posing challenges for accurate annotation and model transfer from healthy individuals to patients with DOC. Existing domain adaptation methods struggle to capture inter-subdomain relationships, resulting in suboptimal cross-domain learning performance. This study proposes a novel Category Maximum Mean Discrepancy (CMMD) subdomain adaptation method for EEG-based emotion recognition and consciousness detection. The CMMD framework aligns subdomain distributions of the same category in target and source domains, capturing subtle EEG features in emotional states and improving cross-domain classification. On the emotion recognition benchmark datasets SEED and SEED-IV, our method outperforms existing baselines, achieving 85.67% and 74.44% accuracy. In clinical experiments on 25 DOC patients, our research explored within-group dynamics among 15 MCS and 10 UWS patients via EEG-based emotion recognition. Experiments revealed higher average emotion recognition accuracy in MCS patients (66.21%) than UWS patients (56.99%), with a positive correlation to CRS-R scores (p<0.01). Our research demonstrated that emotion recognition accuracy in all MCS patients and five UWS patients surpassed chance level. All 15 MCS patients had an average recognition accuracy of 66.21%. Although the accuracy of 5 out of 10 UWS patients ranged from 60.42% to 68.75%, barely exceeding the 59.5-60% significance threshold of chance level, this provides objective neurophysiological evidence for potential residual consciousness, suggesting physicians should avoid overlooking subtle signs of consciousness in UWS patients. Results in clinical application highlight our method's potential in understanding DOC patient subgroups and enhancing consciousness detection.

Electroencephalography (EEG) is a powerful and objective tool for detecting emotions, with broad applications across various domains. However, EEG-based emotion recognition faces two major challenges: (1) extracting domain-invariant features that maintain emotion-related information across variations induced by individual differences, and (2) aligning the marginal and conditional distributions of data from different individuals in the feature space. To address these challenges, we propose a novel method: the Multi-scale Dynamic Temporal Network with Graph Matching Domain Adaptation, designed for cross-subject EEG emotion recognition. Our method employs a multi-scale dynamic temporal attention module to extract robust domain-invariant features. In addition, it utilizes a domain adaptation network based on Chebyshev graph representations, which reformulates the emotion recognition task as a graph matching problem. This approach effectively aligns cross-subject data distribution. To validate the effectiveness of our method, we conducted extensive experiments on two benchmark databases (SEED and DEAP) under three different cross-validation protocols. The results demonstrate consistent reliability in subjects and sessions. Specifically, in the cross-subject single-session cross-validation task, our method achieved an accuracy of 94.69%$\pm$5.16% on the SEED dataset. On the DEAP dataset, our approach achieved accuracies of 69.45%$\pm$7.26% and 67.52%$\pm$8.28% for the Valence and Arousal dimensions, respectively. These results demonstrate that our method outperforms existing state-of-the-art approaches. Source code is available at:https://github.com/seizeall/MDTN-GMDA.

Sleep micro-events are crucial indicators of sleep quality and neurological health. However, traditional sleep micro-events detectors often suffer from low precision, which leads to a high rate of false positives and misclassification. This affects the reliability of sleep research and clinical diagnosis. In this paper, we propose an attention-based neural network using residual blocks named DARN for precise detection of sleep micro-events. It consists of three main parts: 1) local feature extraction: convolutional neural networks (CNNs) with attention mechanism are used for extracting temporal features; 2) residual module: solving the problems of gradient vanishing and network degradation, for precise feature representations learning; and 3) temporal context modelling: bidirectional long short-term memory (Bi-LSTM) units to better capture the contextual relationships in the sleep electroencephalogram (EEG). By conducting experiments on the publicly accessible datasets MASS2 and DREAMS, the proposed architecture achieves a precision of 0.771 and an F1-score of 0.768 for detecting sleep spindles (MASS2), a precision of 0.632 and an F1-score of 0.608 for detecting K-complexes (MASS2), and a precision of 0.642 and an F1-score of 0.616 for detecting sleep spindles (DREAMS). The results show that our method outperforms existing detection methods, laying the foundation for better clinical utility.

Few-Shot Learning (FSL) is a technique aimed at improving a model’s ability to generalize to unseen categories using only a small amount of labeled data. Representative shift vectors and the hubness problem are two common issues that often hinder the performance of FSL. Representative shift vectors refer to the inherent bias caused by the absolute positional differences of various base classes in the feature space. The hubness problem occurs when a class prototype becomes the nearest neighbor for many test instances, regardless of their true class. Inspired by generative models, we find that these two problems can be effectively addressed simultaneously. By leveraging generative models, we eliminate representative shift vectors through learning intra-class diversity and enhance the quality of generated samples by utilizing hubness samples. Experimental results demonstrate that our method improves performance, and our code will be available at https://github.com/cql33/HG-VAE.

In this paper, a novel spatio-temporal self-constructing graph neural network (ST-SCGNN) is proposed for cross-subject emotion recognition and consciousness detection. For spatio-temporal feature generation, activation and connection pattern features are first extracted and then combined to leverage their complementary emotion-related information. Next, a self-constructing graph neural network with a spatio-temporal model is presented. Specifically, the graph structure of the neural network is dynamically updated by the self-constructing module of the input signal. Experiments based on the SEED and SEED-IV datasets showed that the model achieved average accuracies of 85.90% and 76.37%, respectively. Both values exceed the state-of-the-art metrics with the same protocol. In clinical besides, patients with disorders of consciousness (DOC) suffer severe brain injuries, and sufficient training data for EEG-based emotion recognition cannot be collected. Our proposed ST-SCGNN method for cross-subject emotion recognition was first attempted in training in ten healthy subjects and testing in eight patients with DOC. We found that two patients obtained accuracies significantly higher than chance level and showed similar neural patterns with healthy subjects. Covert consciousness and emotion-related abilities were thus demonstrated in these two patients. Our proposed ST-SCGNN for cross-subject emotion recognition could be a promising tool for consciousness detection in DOC patients.

The brain-computer interface (BCI) provides a direct communication pathway between the human brain and external devices. However, the models trained for existing subjects perform poorly on new subjects, which is termed the subject calibration problem. In this paper, we propose a semi-supervised meta learning (SSML) method for subject-transfer calibration. The proposed SSML learns a model-agnostic meta learner with existing subjects and then fine-tunes the meta learner in a semi-supervised learning manner, i.e. using a few labelled samples and many unlabelled samples of the target subject for calibration. It is significant for BCI applications in which labelled data are scarce or expensive while unlabelled data are readily available. Three different BCI paradigms are tested: event-related potential detection, emotion recognition and sleep staging. The SSML achieved classification accuracies of 0.95, 0.89 and 0.83 in the benchmark datasets of three paradigms. The runtime complexity of SSML grows linearly as the number of samples of target subject increases so that is possible to apply it in real-time systems. This study is the first attempt to apply semi-supervised model-agnostic meta learning methodology for subject calibration. The experimental results demonstrated the effectiveness and potential of the SSML method for subject-transfer BCI applications.