Electroencephalogram-based time-frequency analysis for Alzheimer’s disease detection using machine learning

The present study highlights the potential of EEG as a promising tool for early AD diagnosis. Nevertheless, a more extensive database should be used to enhance the generalizability of the results in future work.

Authors

Institutions

Publication Details

Journal
Journal of Biological Methods
Published
2024-11-26
DOI
https://doi.org/10.14440/jbm.2025.0069
Citations
2
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.69

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Electroencephalogram-based time-frequency analysis for Alzheimer’s disease detection using machine learning

Sérgio Daniel Rodrigues, Pedro Henrique Fernandes Rodrigues
2 citations
Journal of Biological Methods
EEG and Brain-Computer Interfaces
0.69
article

Electroencephalogram-based time-frequency analysis for Alzheimer’s disease detection using machine learning

Sérgio Daniel Rodrigues, Pedro Henrique Fernandes Rodrigues
article en
2 citations

Abstract

The present study highlights the potential of EEG as a promising tool for early AD diagnosis. Nevertheless, a more extensive database should be used to enhance the generalizability of the results in future work.

Journal of Biological MethodsVol. 12(1)
Universidade Católica Portuguesa (PT)
Fundação para a Ciência e a Tecnologia
Openalex Percentile: Top 29%
EEG and Brain-Computer Interfaces
0.69
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.