Hyperscanning enables simultaneous electroencephalography (EEG) recording from multiple individuals, facilitating collaborative brain activity to reduce individual biases and enhance the reliability of decision-making. The decoding of such collaborative paradigm tasks has traditionally relied solely on simple fusion methods based on each individual brain activity, without incorporating cross-brain coupling information. Inspired by social interaction studies on enhanced inter-brain synchrony in collaborative tasks using hyperscanning, we propose a joint learning framework for dual-brain target detection that integrates a shared space construction module and shared feature-guided module. The shared space construction module incorporates brain-to-brain coupling analysis to identify cross-brain synchrony, and further integrates shared and private features through a multi-head fusion mechanism for joint representation learning in shared feature-guided module. Experimental results show an average 10% improvement in balanced accuracy across 12 participant groups compared to traditional single-brain approaches, with some groups achieving up to a 5% gain over state-of-the-art (SOTA) methods. Notably, higher-performing groups exhibit stronger inter-brain coupling and more synchronized target-related responses. These findings advance the development of collaborative brain-computer interface (BCI) systems for more robust and effective target detection.

The multi-user motor imagery brain-computer interface (BCI) is a new approach that uses information from multiple users to improve decision-making and social interaction. Although researchers have shown interest in this field, the current decoding methods are limited to basic approaches like linear averaging or feature integration. They ignored accurately assessing the coupling relationship features, which results in incomplete extraction of multi-source information. To overcome these limitations, we propose a new reinforcement learning electroencephalography (EEG) decoding method based on mutual information mechanisms. Our method enhances the extraction of multi-source common information and uses a dynamic feedback model for inter-brain mutual information reward and punishment mechanisms in the reinforcement learning channel selection module. We feed the single-brain and inter-brain signals after channel selection into deep neural networks, which automatically extract coupled features. Finally, based on the attention indices calculated from EEG signals at prefrontal electrode positions, the output is obtained by voting. Our experimental results show that the average accuracy of dual-brain recognition is improved by 16% compared to single-brain mode. Furthermore, ablation experiments demonstrate that the reinforcement learning module and attention voting module enhance accuracy by 14.5% and 15.7%, respectively.

This study investigates the cognitive impact of dynamic icons in mobile interfaces by integrating electroencephalography (EEG) and eye-tracking technologies. Traditional research on mobile app interface design has relied mainly on questionnaire and eye-tracking methods for behavioral analysis. This research adds a new dimension by examining the neural mechanisms associated with dynamic icons. We employed EEG to analyze channel-wise power spectrum density (PSD), focusing on the alpha and theta frequency bands related to attention and working memory. Concurrently, eye-tracking data were analyzed through Areas of Interest (AOIs) and fixation metrics to assess visual attention patterns. The results indicate that dynamic icons significantly enhance neural activity, with a 15% increase in alpha band power and a 20% increase in theta band power compared to static icons. Additionally, eye-tracking data show a 30% increase in total fixation duration on AOIs containing dynamic icons, particularly in the left-top quarter of the mobile interface. This effect was observed without changes in the first fixation duration, suggesting that dynamic icons have a stronger impact on sustained attention rather than on initial capture. These findings highlight that dynamic icons not only attract and maintain visual attention more effectively but also enhance cognitive processing efficiency. This study provides valuable insights for optimizing mobile app interface design, emphasizing the benefits of incorporating dynamic elements to improve user engagement and interface effectiveness.

Clinically, motor rehabilitation and mechanism research have garnered increasing attention. However, cognitive impairments often accompany stroke patients. Mental rotation is a crucial task for cognitive evaluation and training. In our study, we proposed a method to compare mental rotation perception mechanisms between healthy individuals and stroke patients using EEG indicators. The experiment was designed to accommodate stroke patients. In the data analysis module, we utilized power spectral density (PSD) to explore single-channel frequency domain indicators and phase-locked value (PLV) to analyze connectivity between channels. Mechanism analysis based on EEG indicators considered three main conditions: counter-clockwise and clockwise rotation, lesion area versus functional area across strokes, and mental rotation perception sub-stages. Our experimental results show significant differences between the stroke group and health group in the θ and α frequency bands across counter-clock and clock-wise perception and lesion connectivity conditions. The stroke group reveals a compensatory effect in the lesion areas, with higher PLV values (averaging 0.2101) compared to those in common cognitive functional areas of the health group and the connectivity within lesion areas was significantly lower (averaging 0.0310) than that outside the lesion areas. These findings could assist in stroke treatment and monitoring rehabilitation progress.

Small samples and significant inter-subject variability are the two main challenges in current electroencephalogram (EEG) based Motor Imagery (MI) Brain-Computer Interface (BCI) decoding methods. To overcome these challenges, we proposed an EEG-based decoding method for MI intentions in mixed-subject settings with adversarial disentanglement, which utilize the EEG decoding focusing on MI related task information and decrease the inference of inter-subject variability under mixed-subject setting. The method includes three main modules: data augmentation module, dual-label training module, disentanglement training module. It first uses a mixed-subject settings approach, which involves shuffling the data from all subjects to augment the data for a single model. We then create a dual-label dataset using motor imagery labels and identity labels. Finally, a disentanglement training strategy is employed to optimize the negative entropy loss, measuring the inter-subject variability. Our experiment results show that our method achieves higher accuracy compared to traditional one-to-one model training methods and lower variance with the mixed-subject settings. It has achieved a $\mathbf{7 5. 9 3 \%}$ average classification accuracy across four classes on the BCIC-IV-2a dataset with the best classification accuracy reaches $\mathbf{9 0. 2 8 \%}$, indicating that our model has the capability to disentangle identity-related information during the feature extraction and has more stable performance across different subjects.

Cognitive perception is a fundamental human physiological function, closely interrelated with decision-making, belief formation, and problem-solving. Decoding the mechanisms of cognitive perception is crucial. Advances in brain signal processing and imaging have made the study of human cognitive perception more precise and convenient. In this paper, we explore potential common biomarkers in terms of spatial rotation (SR) and working memory (WM) on basis of their similarities. We propose a power spectrum density (PSD)-based analysis pipeline that includes statistical analysis, ratio definition, impact measurement of brain state, and inter-stage analysis. Our experimental results show that the average distribution of PSD is similar across different frequency bands, with corresponding specialization observed during inter-stage analysis.

Accurate decoding finger motor imagery is essential for fine motor control using EEG signals. However, decoding finger motor imagery is particularly challenging compared with ordinary motor imagery. This paper proposed a novel EEG decoding method of feature-dependent frequency band selection, feature fusion, and ensemble learning (DSFE) for finger motor imagery. First, a feature-dependent frequency band selection method based on correlation coefficient (FDCC) was proposed to select feature-specific effective bands. Second, a feature fusion method was proposed to fuse different types of candidate features to produce multiple refined sets of decoding features. Finally, an ensemble model using the weighted voting strategy was proposed to make full use of these diverse sets of final features. The results on a public EEG dataset of five fingers motor imagery showed that the DSFE method is effective and achieves the highest decoding accuracy of 50.64%, which is 7.64% higher than existing studies using exactly the same data. The experiments further revealed that both the effective frequency bands of different subjects and the effective frequency bands of different types of features are different in finger motor imagery. Furthermore, compared with two-hand motor imagery, the effective decoding information of finger motor imagery is transferred to the lower frequency. The idea and findings in this paper provide a valuable perspective for understanding fine motor imagery in-depth.

Graph convolutional network (GCN) based on the brain network has been widely used for EEG emotion recognition. However, most studies train their models directly without considering network dimensionality reduction beforehand. In fact, some nodes and edges are invalid information or even interference information for the current task. It is necessary to reduce the network dimension and extract the core network. To address the problem of extracting and utilizing the core network, a core network extraction model (CWGCN) based on channel weighting and graph convolutional network and a graph convolutional network model (CCSR-GCN) based on channel convolution and style-based recalibration for emotion recognition have been proposed. The CWGCN model automatically extracts the core network and the channel importance parameter in a data-driven manner. The CCSR-GCN model innovatively uses the output information of the CWGCN model to identify the emotion state. The experimental results on SEED show that: 1) the core network extraction can help improve the performance of the GCN model; 2) the models of CWGCN and CCSR-GCN achieve better results than the currently popular methods. The idea and its implementation in this paper provide a novel and successful perspective for the application of GCN in brain network analysis of other specific tasks.

In recent years, electrocardiogram (EEG)-based emotion recognition has received increasing attention in affective computing. Since the individual differences of EEG signals are large, most models are trained for specific subjects, and the generalization is poor when applied to new subjects. In this paper, we propose a Multi-Branch Network (MBN) model to solve this problem. According to the characteristics of the cross-subject data, different branch networks are designed to separate the background features and task features of the EEG signals for classification to have better model performance. Besides, there is no new-subject data needed during model training. In order to avoid the negative improvement caused by samples with significant differences to model training, a tiny amount of new-subject data is used to filter the training samples to improve the model performance further. Before training the model, the samples with significant differences from the new subject were deleted by comparing the background features between the subjects. The experimental results show that compared with Single-Branch Network (SBN) model, the accuracy of the MBN model is improved by 20.89% on the SEED dataset. Furthermore, compared with other common methods, the proposed method uses less new-subject data, which improves its practicability in practical application.

Traditional speaker recognition is based on the individual difference information in the voice’s acoustic parameters aroused by the structural characteristics of vocal organs. However, it only focuses on the recognition accuracy from the speaker-side without the listeners’ side. In this paper, we explored the voice evoked EEG (electroencephalography)-based speaker recognition with experiment design, feature extraction, classification and channel selection. The subject (listener) was asked to listen the audio-text stimuli consisted of four speakers and the subject’s EEG was used to decode the speakers. The extracted time-domain and time-frequency domain features were fed into siamese network in which the inter-class and intraclass samples were both trained. The empirical results show that 1) delta band (0.1-3 Hz) and high gamma band (51-80 Hz) provide higher recognition accuracy among other frequency bands; 2) frontal and parietal lobes play an important role; 3) the recognition performance is improved with subject’s attention; 4) when the listener is familiar with the speakers, the recognition accuracy is significantly higher than unfamiliar case. This work provides the brain mechanism and data processing method for auditory brain computer interface (BCI).

Multi-user BCI (brain computer interface) refers to a kind of BCI in which there are several users participating in one task. Differently from P300 and SSVEP (steady-state visual evoked potential), MI (motor imagery)-BCI does not rely on external stimulus, which is more widely used in the assistance of disabled people. The main problem of MI-BCI is to achieve asynchronous control, which needs to improve the rest state recognition accuracy. We proposed a CNN-based approach for three-class classification of motor imagery EEG data including rest state in hybrid multi-user BCI. Firstly, we designed a two-user hybrid MI-BCI experimental paradigm and moreover proposed a CNN-based processing framework method which contains several strategies for inter-brain phase locked value (PLV) features fusion. Results show that the alpha band shows the significantly better performance than other bands and the classification performance of multi-user MI is better than single-user MI. Multi-user BCI is a potential way to enhance the performance of asynchronous MI-BCI.

Gender recognition is widely studied in different areas and especially, many physiological theories have shown that there are many differences in emotional processing between different genders. However, there are many observations need to be verified. In our paper, we focus on the gender recognition in emotion perception using diverse EEG (electroencephalogram) features. The time-frequency and phase locked value (PLV) features are extracted and different feature selection methods are compared. We performed these methods on DEAP dataset. Results show that 1) the gender recognition accuracy using PLV feature is higher than using traditional time-frequency feature. 2) extreme learning machine (ELM) classifier has the best performance. 3) the gender recognition accuracy of theta and gamma bands is higher than other bands, which indicates that theta and gamma bands contain more discriminate gender information. 4) arousal has a greater impact than valence. And recognition accuracy of `calm' emotion is lower than other emotions 5) feature selection methods can improve the accuracy.

Motor imagery (MI) is a kind of spontaneous controlled brain computer interface (BCI) paradigm, which is more likely to the concept of 'mind control'. The idle state detection is an important problem to construct a robust MI-BCI system since it needs to tell whether the subject is in MI task and the idle state contains much diverse cases. Herein, EEG-based multi-user BCI refers to two or more subjects engage in a coordinate task while their EEG are simultaneously recorded. The objective of this paper is to explore how the multi-user MI-BCI performance in idle detection based on CSP (common spatial pattern) and brain-network features. We proposed several strategies for cross-brain feature fusion. Results show that 1) Through CSP features, the classification accuracy of cross-brain outperforms the single brain CSP feature across different strategies. 2) Through brain-network features, the classification accuracy of concatenated with the paired subjects outperforms the single brain-network, while the inter-brain-network is lower than single subject 3) alpha frequency band shows better performance than other bands. Multi-user MI-BCI would be a potential way to improve the idle state detection accuracy.