Advances in Brain-Computer Interfaces and Neuroimaging

The field of brain-computer interfaces (BCIs) and neuroimaging is rapidly advancing, with a focus on improving the accuracy and interpretability of neural signal decoding. Recent developments have led to the creation of new frameworks and models that can better capture the complexities of brain activity and improve the performance of BCIs. One of the key areas of research is the use of multimodal functional neuroimaging, which enables the systematic analysis of brain mechanisms and provides discriminative representations for BCI decoding. Additionally, there is a growing interest in the use of generative models and operator learning to improve the accuracy and robustness of BCIs. Noteworthy papers in this area include:

  • A study that proposes a neuro-symbolic operator framework for characterizing complex piezoelectric systems, which enables accurate and interpretable prediction of displacement profiles.
  • A paper that introduces a unified representation framework for multimodal functional neuroimaging via generative artificial intelligence, which can generate data for acquisition-constrained modalities and underrepresented groups.
  • A research work that presents a pre-trained generative framework for unified representation of neural signals, which can generate data consistent with real brain activity patterns and improve performance on downstream tasks.

Sources

Category-aware EEG image generation based on wavelet transform and contrast semantic loss

Neuro-Symbolic Operator for Interpretable and Generalizable Characterization of Complex Piezoelectric Systems

Crack Path Prediction with Operator Learning using Discrete Particle System data Generation

LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale

Empowering Functional Neuroimaging: A Pre-trained Generative Framework for Unified Representation of Neural Signals

GANORM: Lifespan Normative Modeling of EEG Network Topology based on Multinational Cross-Spectra

NOBLE -- Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models

Neuronal avalanches as a predictive biomarker of BCI performance: towards a tool to guide tailored training program

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