Domain-Incremental Learning Framework for Continual Motor Imagery EEG Classification Task.

Due to inter-subject variability in electroencephalogram (EEG) signals, the generalization ability of many existing brain-computer interface (BCI) models is significantly limited. Although transfer learning (TL) offers a temporary solution, in scenarios requiring sustained knowledge transfer, the performance of TL-based models gradually declines as the number of transfers increases-a phenomenon known as catastrophic forgetting. To address this issue, we introduce a novel domain-incremental learning framework for the continual motor imagery (MI) EEG classification.

Baseline-Guided Representation Learning for Noise-Robust EEG Signal Classification.

Brain-computer interfaces (BCIs) suffer from limited accuracy due to noisy electroencephalography (EEG) signals. Existing denoising methods often remove artifacts such as eye movement or use techniques such as linear detrending, which inadvertently discard crucial task-relevant information. To address this issue, we present BGNet, a novel deep learning framework that leverages underutilized baseline EEG signals for dynamic noise mitigation and robust feature extraction to improve motor imagery (MI) EEG classification.

Developing Brain-Based Bare-Handed Human-Machine Interaction via On-Skin Input.

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.

Long-Term Depression Induced by Optogenetically Driven Nociceptive Inputs to Trigeminal Nucleus Caudalis or Headache Triggers.

The peripheral trigeminovascular pathway mediates orofacial and craniofacial pain and projects centrally to the brainstem trigeminal nucleus caudalis (TNc). Sensitization of this pathway is involved in many pain conditions, but little is known about synaptic plasticity at its first central synapse. We have taken advantage of optogenetics to investigate plasticity selectively evoked at synapses of nociceptive primary afferents onto TNc neurons.