NRGAMTE: Neurophysiological Residual Gated Attention Multimodal Transformer Encoder for Sleep Disorder Detection

The results shows that NRGAMTE obtains high performance across multiple datasets, significantly improving detection accuracy. This demonstrated their potential as a reliable tool for clinical sleep disorder detection.

Authors

Institutions

Publication Details

Journal
Brain Sciences
Published
2025-09-13
DOI
https://doi.org/10.3390/brainsci15090985
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

NRGAMTE: Neurophysiological Residual Gated Attention Multimodal Transformer Encoder for Sleep Disorder Detection

Janani Subramaniam, Amith Vijaykumar, Aruna Mogarala Guruvaya, Puttamadappa Chaluve Gowda
Brain Sciences
EEG and Brain-Computer Interfaces
article

NRGAMTE: Neurophysiological Residual Gated Attention Multimodal Transformer Encoder for Sleep Disorder Detection

Janani Subramaniam, Amith Vijaykumar, Aruna Mogarala Guruvaya, Puttamadappa Chaluve Gowda
article en

Abstract

The results shows that NRGAMTE obtains high performance across multiple datasets, significantly improving detection accuracy. This demonstrated their potential as a reliable tool for clinical sleep disorder detection.

Brain SciencesVol. 15(9)
Centre for Artificial Intelligence and Robotics (IN), Salem College (US), Dr. Hari Singh Gour University (IN)
Openalex Percentile: Top 75%
EEG and Brain-Computer Interfaces
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.