Abstract: The channel mode of OCDMA system is interpreted first. Then a novel decoding of convolutional codes is proposed according to the characteristics of OCDMA system. And the feasibility of this ...

This repository contains the implementation of our paper: "FAConformer: Frequency-Aware Convolutional Transformer for Auditory Attention Decoding", serving as a benchmark codebase for auditory ...

Abstract: This paper introduces a convolutional syndrome former (CSF) which enables reduced complexity decoding of regularly punctured convolutional codes (CCs) of rate r/(r + 1). The CSF is derived ...

ECoG signals are widely used in Brain-Computer Interfaces (BCIs) due to their high spatial resolution and superior signal quality, particularly in the field of neural control. ECoG enables more ...

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-learning algorithms trained ...

description [NeurIPS 2025][Segmentation][diffusion language model] By introducing convolutional decoding normalization (replacing hard semi-autoregressive chunking) and rule-based rejective ...

High inter-subject variability and the non-stationary nature of EEG signals pose significant challenges for subject-independent Brain-Computer Interfaces (BCIs) leading to poor model generalization.

Researchers have demonstrated how to decode what the human brain is seeing by using artificial intelligence to interpret fMRI scans from people watching videos, representing a sort of mind-reading ...

I’m thrilled to share that our work on Diffusion LMs has been accepted at NeurIPS (spotlight)! --- Introducing: [Fast and Fluent Diffusion Language Models via Convolutional Decoding and Rejective Fine ...

WEST LAFAYETTE, Ind. – Researchers have demonstrated how to decode what the human brain is seeing by using artificial intelligence to interpret fMRI scans from people watching videos, representing a ...