In this work, we have proposed a framework for synthesizing the images from the brain activity recorded by an electroencephalogram (EEG) using small-size EEG datasets. This brain activity is recorded ...

DreamDiffusion leverages pre-trained text-to-image models and employs temporal masked signal modeling to pre-train the EEG encoder for effective and robust EEG representations. Additionally, the ...

This sample dataset can also serve as a standard validation dataset for M/EEG methods, hence favoring ... The MNE-Python code is thus provided under the very permissive open ... and Gross, J. (2009).

In the Python ecosystem, MNE-Python offers a unified interface to a variety of EEG file formats and preprocessing routines (Gramfort et al., 2014). Here, we rely on the preprocessed data provided with ...

In this work, we propose a CNN-based approach for classification of short duration EEG signals for visual brain decoding. These signals are captured for a visual perception task by showing digit ...

Piramidal’s founders believe — and claim to know, though this culmination of their work is not yet published — that a foundational model for EEG readings could make lifesaving brain wave ...

Reconstructing images using brain signals of imagined visuals may provide an augmented vision to the disabled, leading to the advancement of Brain-Computer Interface (BCI) technology. The recent ...