This paper presents the first quantitative analysis of China's Brain-Computer Interface (BCI) translational ecosystem, examining clinical trials, investigator-initiated trials, and regulatory-approved…

EvoBrain proposes a dynamic, cross-task continual learning framework to overcome the limitations of task-specific EEG decoding, enabling unified and scalable brain-computer interfaces.

EVA-Net proposes a two-stage framework that uses action videos as semantic priors to achieve strong subject-independent EEG motor decoding, significantly outperforming text-based methods.

The paper designed a minimalist BCMI system to translate EEG-measured emotional valence into adaptive music, but preliminary testing showed that frontal alpha asymmetry was not reliably modulated by i…

This paper evaluates the use of EEG Foundation Models for burst suppression detection in ICU EEG data, achieving state-of-the-art performance.

This paper proposes a lightweight CNN architecture that significantly enhances the adversarial robustness of EEG-based Brain-Computer Interfaces (BCIs) against malicious perturbations.

This paper reviews the limitations of Deep Learning models in EEG analysis for epilepsy diagnosis and proposes Kolmogorov-Arnold Networks (KANs) as a solution.

This paper investigates neural activity during five auditory conditions using EEG recordings from a 5-year-old participant, revealing condition-specific modulation of neural oscillatory activity and d…

This paper benchmarks five positional encoding strategies for transformer-based EEG foundation models, concluding that the optimal encoding is task-dependent and no single strategy is universally supe…

CaMBRAIN introduces a novel Mamba-based State Space Model (SSM) for real-time, continuous EEG inference, achieving state-of-the-art results with significantly higher throughput than existing methods.

This paper compares different decoding pipelines for motor imagery tasks using EEG data and finds that no single pipeline dominates, emphasizing the need for participant-aware model selection.

This paper investigates a novel vulnerability in tactile sensing by demonstrating that targeted Electromagnetic Interference (EMI) can induce strong, misleading 'phantom forces' in Hall-effect fingert…

The paper introduces a compact, dispersive RC circuit model for electro-quasi-static (EQS) head modeling, accurately representing the brain, skull, and scalp layers for brain-oriented applications.

The paper introduces Dual-Spectral Flow Matching (DSFM), a novel generative framework that uses wavelet and cosine transforms to synthesize highly realistic, non-stationary fMRI time series for improv…

The paper proposes a multi-dimensional evaluation framework to assess EEG foundation models under realistic low-resource conditions, finding that while these models excel in long-context tasks, their…

The paper introduces Spike-PTSD, a novel, biologically inspired adversarial attack framework that successfully compromises the robustness of Spiking Neural Networks (SNNs) by modeling abnormal neural…

The paper proposes the Morlet Spectral Transformer (MST), a novel architecture that effectively decodes cross-subject emotion from EEG by designing specialized spectral and spatial representations, ou…

The paper proposes EEG-FuseFormer, a transformer-based framework that fuses features from CNN-LSTM and ResNet-18 to achieve high accuracy in predicting seizure onset from EEG signals.

This paper provides a comparative framework analyzing the distinct security and privacy risks inherent in virtual and robotic assistive systems, culminating in design recommendations for trustworthy t…

Mind-Omni introduces a unified multi-task framework that models the interplay between brain, vision, and language signals using a discrete diffusion paradigm, achieving state-of-the-art performance ac…