Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

Coders have had a field day weeding through the treasures in the Claude Code leak. "It has turned into a massive sharing party," said Sigrid Jin, who created the Python edition, Claw Code. Here's how ...

There was a time when extraction shooters felt like a niche obsession reserved for the most hardcore players. They often felt intimidating for anyone used to traditional shooters, where respawns came ...

(A) Overall structure of the model. MLP, multilayer perceptron. (B) Structure of the time encoder module. (C) Structure of the channel encoder module. BN, batch normalization. “Domain bias caused by ...

Abstract: Electroencephalogram (EEG) signals to classify sleep stages have emerged as a vital area of research, aiming to provide non-invasive measures of people's neurological and cognitive states.

Summary: New research shows that deep learning can use EEG signals to distinguish Alzheimer’s disease from frontotemporal dementia with high accuracy. By analyzing both the timing and frequency of ...

Researchers at örebro University have developed two new AI models that can analyze the brain's electrical activity and accurately distinguish between healthy individuals and patients with dementia, ...

Researchers at Tsinghua University developed the Optical Feature Extraction Engine (OFE2), an optical engine that processes data at 12.5 GHz using light rather than electricity. Its integrated ...