State space models (SSMs) have demonstrated state-of-the-art sequence modeling
performance in some modalities, but underperform attention in language modeling …

Decoding speech from brain activity is a long-awaited goal in both healthcare and
neuroscience. Invasive devices have recently led to major milestones in this regard: deep …

State space models (SSMs) have high performance on long sequence modeling but require
sophisticated initialization techniques and specialized implementations for high quality and …

In mental state decoding, researchers aim to identify the set of mental states (eg,
experiencing happiness or fear) that can be reliably identified from the activity patterns of a …

In the past five years, the use of generative and foundational AI systems has greatly
improved the decoding of brain activity. Visual perception, in particular, can now be decoded …

To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-
Computer Interface (BCI) tasks, and to harness the power of large publicly available data …

Electroencephalography (EEG) is a prominent non-invasive neuroimaging technique
providing insights into brain function. Unfortunately, EEG data exhibit a high degree of noise …

The exploration of brain activity and its decoding from fMRI data has been a longstanding
pursuit, driven by its potential applications in brain-computer interfaces, medical diagnostics …

The analysis and understanding of brain characteristics often require considering region-
level information rather than voxel-sampled data. Subject-specific parcellations have been …

Deep learning (DL) models find increasing application in mental state decoding, where
researchers seek to understand the mapping between mental states (eg, experiencing …