Current noninvasive electroencephalography (EEG)-based brain–computer interface (BCI) systems face a fundamental scalability barrier: they either suffer catastrophic forgetting (CF) when learning from new users or require centralized management and use of sensitive neural data from previous users-making real-world deployment impractical. To address this, we introduce subject-incremental continual adaptation (SI-CA), a novel paradigm that models cross-subject continual learning (CL), where knowledge transfer and limited replay sustain stable performance as new subjects are introduced, enabling continual decoding without forgetting. Building on this paradigm, we propose a novel CL framework that achieves memory-efficient adaptation by integrating an extendable architecture with prototype-based consistency regularization and limited replay to mitigate CF. The effectiveness of our proposed method has been validated on three benchmark EEG-BCI datasets. Experimental results demonstrate that the proposed method can effectively reduce reliance on historical samples during CL, while maintaining stable decoding performance for previously learned individuals and ensuring reliable motor decoding for newly encountered ones. This holds significant importance for the development of scalable, privacy-preserving, and stable neural interface systems.

Electroencephalography (EEG) is a fundamental modality for cognitive state monitoring in brain-computer interfaces (BCIs). However, it is highly susceptible to intrinsic signal errors and human-induced labeling errors, which lead to label noise and ultimately degrade model performance. To enhance EEG learning, multimodal knowledge distillation (KD) has been explored to transfer knowledge from visual models with rich representations to EEG-based models. Nevertheless, KD faces two key challenges: modality gap and soft label misalignment. The former arises from the heterogeneous nature of EEG and visual feature spaces, while the latter stems from label inconsistencies that create discrepancies between ground truth labels and distillation targets. This paper addresses semantic uncertainty caused by ambiguous features and weakly defined labels. We propose a novel cross-modal knowledge distillation framework that mitigates both modality and label inconsistencies. It aligns feature semantics through a prototype-based similarity module and introduces a task-specific distillation head to resolve label-induced inconsistency in supervision. Experimental results demonstrate that our approach improves EEG-based emotion regression and classification performance, outperforming both unimodal and multimodal baselines on a public multimodal dataset. These findings highlight the potential of our framework for BCI applications.

Conventional reinforcement learning (RL) approaches often struggle to learn effective policies under sparse reward conditions, necessitating the manual design of complex, task-specific reward functions. To address this limitation, reinforcement learning from human feedback (RLHF) has emerged as a promising strategy that complements hand-crafted rewards with human-derived evaluation signals. However, most existing RLHF methods depend on explicit feedback mechanisms such as button presses or preference labels, which disrupt the natural interaction process and impose a substantial cognitive load on the user. We propose a novel reinforcement learning from implicit human feedback (RLIHF) framework that utilizes non-invasive electroencephalography (EEG) signals, specifically error-related potentials (ErrPs), to provide continuous, implicit feedback without requiring explicit user intervention. The proposed method adopts a pre-trained decoder to transform raw EEG signals into probabilistic reward components, enabling effective policy learning even in the presence of sparse external rewards. We evaluate our approach in a simulation environment built on the MuJoCo physics engine, using a Kinova Gen2 robotic arm to perform a complex pick-and-place task that requires avoiding obstacles while manipulating target objects. The results show that agents trained with decoded EEG feedback achieve performance comparable to those trained with dense, manually designed rewards. These findings validate the potential of using implicit neural feedback for scalable and human-aligned reinforcement learning in interactive robotics.

Malignant arrhythmias (MA), stemming from abnormalities in the neuronal signaling of the cardiac muscle , necessitate sophisticated predictive models for effective clinical management. Traditional machine learning models primarily rely on single-step forecasting and fail to capture the complex temporal dynamics of underlying arrhythmogenic processes. This paper propose the first multi-step forecasting framework for MA, leveraging a brain-inspired approach that emulates and captures the neuronal signal transmission patterns embedded in electrocardiogram (ECG) data. Our framework comprises three key modules: (i) input module, (ii) multi-path propagation module, and (iii) multi-step forecasting module. The multi-path propagation module incorporates short-term and long-term paths that reflect the different time scales of neural information processing . We further introduce novel brain-inspired information processing units within this module. First, the local and global synaptic plasticity units extract the local and global temporal patterns in the ECG using temporal convolution blocks and cosine-similarity based pattern matching. The processed information is transmitted to the subsequent unit, as well as the Hebb-based learning unit, designed to model the neuromodulation of spike- and feature-level activations and connection strength of the pre- and post-synaptic neurons. Evaluated on two benchmark datasets, our model outperforms existing state-of-the-art models and baseline multi-step models in both short-term and long-term forecasting tasks. The results not only demonstrate the potential of our model in providing a robust clinical tool for fine-grained arrhythmia intervention but also offer valuable insights for advancing multi-step forecasting in other applications.

Developing natural, intuitive, and human-centric input systems for mobile human-machine interaction (HMI) poses significant challenges. Existing gaze or gesture-based interaction systems are often constrained by their dependence on continuous visual engagement, limited interaction surfaces, or cumbersome hardware. To address these challenges, we propose MetaSkin, a novel neurohaptic interface that uniquely integrates neural signals with on-skin interaction for bare-handed, eyes-free interaction by exploiting human's natural proprioceptive capabilities. To support the interface, we developed a deep learning framework that employs multiscale temporal-spectral feature representation and selective feature attention to effectively decode neural signals generated by on-skin touch and motion gestures. In experiments with 12 participants, our method achieved offline accuracies of 81.95% for touch location discrimination, 71.00% for motion type identification, and 46.08% for 10-class touch-motion classification. In pseudo-online settings, accuracies reached 99.43% for touch onset detection, and 80.34% and 67.02% for classification of touch location and motion type, respectively. Neurophysiological analyses revealed distinct neural activation patterns in the sensorimotor cortex, underscoring the efficacy of our multiscale approach in capturing rich temporal and spectral dynamics. Future work will focus on optimizing the system for diverse user populations and dynamic environments, with a long-term goal of advancing human-centered, neuroadaptive interfaces for next-generation HMI systems. This work represents a significant step toward a paradigm shift in design of brain-computer interfaces, bridging sensory and motor paradigms for building more sophisticated systems.

Human–machine interfaces (HMIs) face several challenges that hinder their long-term performance and adaptability, such as severe overfitting of machine learning models due to limited calibration data on individual users, data distribution shifts owing to changes in user-state over time, and disruptions from outlier samples caused by user distractions. To address this, we propose a novel framework called brain-guided self-paced curriculum learning (BG-SPCL) that leverages user-state information to effectively constrain the learning space for the user intention decoder (curriculum learning) and dynamically adapts the learning curriculum based on the decoder state information self-paced learning (SPL). In the curriculum learning stage, we extract the level of user distraction from brain signals and determine the feasible curriculum region. In the SPL stage, sample difficulty is inferred from the decoder loss, and the sample weights are dynamically adjusted such that the decoder progressively learns more difficult samples. We evaluated the effectiveness of our approach by conducting extensive experiments on three public brain–machine interface (BMI) benchmarks, which constitute an HMI scenario where the user’s brain signals are naturally available. Our results showed superior performance of the proposed method compared to baseline in both offline and online learning settings with no labeled user data, demonstrating the potential for practical application of our framework in both BMI and HMI systems. Our code is available at:https://github.com/yeonoi3488/bg-spcl.