Individuals with severe motor impairments face significant challenges operating conventional interfaces due to reliance on physical movement. While brain–computer interfaces (BCIs) offer alternative control pathways, traditional paradigms (SSVEP/P300) suffer from stimulus dependency, high latency, and cognitive fatigue, limiting real-world deployment. This study presents a novel asynchronous motor imagery-BCI system for hands-free robotic platform navigation, integrating cognitive ergonomics principles to enhance operator experience. We developed an intuitive four-command control paradigm using a cascaded classifier architecture, eliminating dependence on external triggers. Key innovations include: 1) sub-100 ms ultralow-latency pipeline via hybrid feature fusion and ensemble learning; 2) stimulus-independent operation leveraging endogenous sensorimotor cortex activation (μ/β-band); and 3) cognitive load-optimized interaction with ROS-based neurofeedback and NASA-TLX-validated ergonomic design. Evaluated on BCI Competition IV 2a dataset (nine subjects) and self-collected high-resolution electroencephalography data (seven subjects), the system achieved 72.15% offline classification accuracy and 95.36% online command execution success rate with 97ms mean computational latency. NASA-TLX evaluation revealed a 32% reduction in cognitive workload compared to synchronous paradigms. This work establishes a framework for cognitively enhanced mobility assistance, advancing practical brain-controlled assistive technologies.

Motion planning in high-dimensional continuous spaces remains challenging due to complex environments and computational constraints. Although learning-based planners, especially graph neural network (GNN)-based, have significantly improved planning performance, they still struggle with inaccurate graph construction and limited structural reasoning, constraining search efficiency and path quality. The human brain exhibits efficient planning through a two-stage Perception-Decision model. First, egocentric spatial representations from visual and proprioceptive input are constructed, and then semantic–episodic synergy is leveraged to support decision-making in uncertainty scenarios. Inspired by this process, we propose NeuroMP, a brain-inspired planning framework that learns to plan like the human brain. NeuroMP integrates a Perceptive Segment Selector inspired by visuospatial perception to construct safer graphs, and a Global Alignment Heuristic guide search in weakly connected graphs by modeling semantic-episodic synergistic decision-making. Experimental results demonstrate that NeuroMP significantly outperforms existing planning methods in efficiency and quality while maintaining a high success rate.

Research on emotion recognition in conversations emphasises the importance of complex relationships between conversational context and multimodality. Graph-based methods, particularly hypergraph-based methods have shown promise in capturing these relationships. However, challenges persist in avoiding redundant context while capturing essential information for optimal context embeddings and fully leveraging cross-modal complementarities for sufficient fusion. In contrast, the human brain flexibly retrieves relevant memories and integrates multi-modal data for accurate recognition. Based on this superiority, we propose BrainyHGNN, a brain-inspired hypergraph neural network. It integrates a Dynamic Memory Selector for contextual hyperedges, mimicking selective memory retrieval mechanisms for adaptive and modality-specific context retrieval. HierSensNet is designed for multi-modal hyperedges, mirroring hierarchical cross-modal interaction mechanisms to ensure effective multimodal fusion. Experimental results on two benchmark datasets validate the superior performance of BrainyHGNN, confirming the effectiveness of its innovative approach. This work highlights the potential of brain-inspired methods to advance flexible context retrieval and sufficient multimodal fusion, presenting a promising direction for future research in this domain.

Motion planning, considered a fuzzy sequential decision-making problem, encounters significant challenges due to inherent environmental uncertainty. Traditional planning methods that rely on single strategies often struggle in complex scenarios. While fuzzy systems excel at handling uncertainty, high-dimensional continuous spaces require a large number of fuzzy rules, which significantly increases computational complexity. In contrast, humans leverage limited and fuzzy information to address various decision-making scenarios flexibly and efficiently. The concurrent reasoning mechanism in the prefrontal cortex plays a crucial role during this process. Consequently, the brain-inspired model and the concept of multiple fuzzy rules offer a novel perspective for the above issues. Motivated by these insights, this article proposes a brain-inspired motion planning method called harmonized learning with concurrent arbitration (HLCA). Specifically, inspired by the concurrent inference model, a concurrent arbitration module is employed in the planning process to effectively manage the boundary between exploration and exploitation. Furthermore, inspired by the multistrategy processing mechanism, HLCA introduces multistrategy harmonized learning by referring to the mechanism for operating multiple fuzzy rules, allowing the dynamic selection of strategies through a reliability function to enable self-improving learning. Experimental results demonstrate that HLCA outperforms state-of-the-art benchmarks, highlighting its potential to enhance the planning performance of robots by learning from the human brain.

Efficient and reliable motion planning is essential for robots in transportation systems. Learning-based motion planners, especially those leveraging graph neural networks (GNNs), have emerged as promising approaches to accelerate motion planning. However, current GNN-based planners suffer from inevitable performance bottlenecks due to their insufficient exploitation of environmental information and the intrinsic connections within graph structures. In contrast, the human brain exhibits innate strengths in decision-making and reasoning, enabling complex inferences from sparse observations and rapid integration of new information to control behavior. Neuroscience evidence reveals the human brain translates decision-making problems into graph structures and sensory observations, leveraging spatial relational memory for sensory inference. To address these issues, this paper proposes a brain-inspired GNN-based motion planner, BrainyMP, which innovatively draws on the brain’s spatial relational memory mechanisms. Specifically, a selective sampling strategy is proposed to reduce unnecessary exploration during the construction of the random geometric graph (RGG). Additionally, a Brainy Edge Selector is designed to filter inappropriate edges, enhancing planning quality. Furthermore, the Memory-aware Predictor is proposed to improve graph pattern learning capabilities and planning efficiency by integrating subgraph structures. Extensive experimental results demonstrate that the proposed method significantly improves planning quality and efficiency in maze and robotic-arm manipulation tasks while maintaining high success rates. These results indicate that emulating human brain mechanisms holds promise for improving robotic performance.

A major challenge in motor imagery Brain-Computer Interfaces (MI-BCIs) arises from domain shift due to large individual differences. Currently, most cross-subject MI-BCI decoding methods rely on transfer learning to extract subject-shared features or align data distributions. However, these methods typically require all unlabeled data from the target subjects or labeled calibration data, which is unavailable in practical applications. To address this, we propose a brain lateralization-guided subject adaptive network, BLSAN, to enhance model generalization through local-global adversarial training. Specifically, two separate adversarial networks for left and right hemispheres are designed to reduce local differences, and features extracted from both hemispheres are combined for global adversarial training. Additionally, we design a confidence-based pseudo label generation method to enhance model discriminability. We validate the effectiveness of our approach on two public MI datasets, BCI Competition IV 2a and 2b, only with some unlabeled calibration data, which improves the practicality of MI-BCIs.

Neural decoding aims to extract information from neurons' activities to reveal how the brain functions. Due to the inherent spatial and temporal characteristics of brain signals, spatio-temporal computing has become a hot topic for neural decoding. However, the extant spatio-temporal decoding methods usually use static brain topology, ignoring the dynamic patterns of the interaction between brain regions. Further, they do not identify the hierarchical organization of brain topology, leading to only superficial insight into brain spatio-temporal interactions. Therefore, here we propose a novel framework, the Multi-Scale Spatio-Temporal framework with Adaptive Brain Topology Learning (MSST-ABTL), for neural decoding. It includes two new capabilities to enhance spatio-temporal decoding: i) ABTL module, which learns dynamic brain topology while updating specific patterns of brain regions, ii) MSST module, which captures the association of spatial pattern and temporal evolution, and further enhances the interpretability of the learned dynamic topology from multi-scale perspective. We evaluated the framework on the public Human Connectome Project (HCP) dataset (resting-state and task-related fMRI data). The extensive experiments show that the proposed MSST-ABTL outperforms state-of-the-art methods on four evaluation metrics, and also can renew the neuroscientific discoveries in the brain's hierarchical patterns.

A common drawback of EEG-based emotion recognition is that volume conduction effects of the human head introduce interchannel dependence and result in highly correlated information among most EEG features. These highly correlated EEG features cannot provide extra useful information, and they actually reduce the performance of emotion recognition. However, the existing feature selection methods, commonly used to remove redundant EEG features for emotion recognition, ignore the correlation between the EEG features or utilize a greedy strategy to evaluate the interdependence, which leads to the algorithms retaining the correlated and redundant features with similar feature scores in the EEG feature subset. To solve this problem, we propose a novel EEG feature selection method for emotion recognition, termed global redundancy minimization in orthogonal regression (GRMOR). GRMOR can effectively evaluate the dependence among all EEG features from a global view and then select a discriminative and nonredundant EEG feature subset for emotion recognition. To verify the performance of GRMOR, we utilized three EEG emotional data sets (DEAP, SEED, and HDED) with different numbers of channels (32, 62, and 128). The experimental results demonstrate that GRMOR is a promising tool for redundant feature removal and informative feature selection from highly correlated EEG features.

Functional magnetic resonance imaging (fMRI) is a methodology for measuring human brain activities. It has become more and more popular in neural decoding due to its noninvasive. Neural decoding aims to establishing models to reconstruct external stimuli or features of stimuli from known brain responses, so that we can understand the principles of brain functions such as emotion, cognition and language. Neural decoding based on fMRI is of great significance for further understanding the mechanism of brain operation. Most existing studies take multi-scale topology information of brain networks obtained from fMRI into account in neural decoding. However, they always ignore the simultaneous modeling of network structure and hemodynamic response, thus leading to information loss. In addition, current multi-scale methods usually only utilize spatial or logical reasoning relationship of brain networks, which brings challenge to precise neural decoding. In this work, we present a novel and robust multi-scale spatial and logical reasoning learning framework (MSLR) for fMRI-based neural decoding. Specifically, we first design graph signal wavelet generation module to combine brain network topology and node information to construct multi-scale representation of brain networks in a local to global manner. Then, we develop multi-scale information fusion module that can simultaneously model the spatial and logical reasoning relationship of brain networks, it can also learn discriminative multi-scale features with brain state transition. Finally, we construct a neural decoding module to predict the brain states. We evaluated the framework on the public Human Connectome Project (HCP) dataset that included 986 participants. The experimental results with support vector machine (SVM) outperform current state-of-the-art methods on four evaluation metrics (accuracy: 91.58, kappa coefficient: 0.883, macro F1: 0.865 and hamming distance: 0.105) under 19 different stimuli spanning 7 different cognitive tasks. The interpretation of the learned multi-scale representation replicates neuroscientific findings from previous fMRI studies and renews the multi-scale information flow pattern of brain network in neural decoding.

Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder characterized by restricted interests and repetitive behaviors. Non-invasive measurements of brain activity with functional magnetic resonance imaging (fMRI) have demonstrated that the abnormality in the default mode network (DMN) is a crucial neural basis of ASD, but the time-frequency feature of the DMN has not yet been revealed. Hilbert-Huang transform (HHT) is conducive to feature extraction of biomedical signals and has recently been suggested as an effective way to explore the time-frequency feature of the brain mechanism. In this study, the resting-state fMRI dataset of 105 subjects including 59 ASD participants and 46 healthy control (HC) participants were involved in the time-frequency clustering analysis based on improved HHT and modified k-means clustering with label-replacement. Compared with HC, ASD selectively showed enhanced Hilbert weight frequency (HWF) in high frequency bands in crucial regions of the DMN, including the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC) and anterior cingulate cortex (ACC). Time-frequency clustering analysis revealed altered DMN organization in ASD. In the posterior DMN, the PCC and bilateral precuneus were separated for HC but clustered for ASD; in the anterior DMN, the clusters of ACC, dorsal MPFC, and ventral MPFC were relatively scattered for ASD. This study paves a promising way to uncover the alteration in the DMN and identifies a potential neuroimaging biomarker of diagnostic reference for ASD.