Multi-modality NDE fusion using encoder–decoder networks for identify multiple neurological disorders from EEG signals

This innovative approach represents a substantial advancement in the field of neurological disorder diagnosis. By integrating diverse NDE data with EEG signals through advanced neural network techniques, we have developed a method that enhances the accuracy and efficiency of diagnosing multiple neurological conditions. This fusion of multi-modality data has the potential to revolutionize current diagnostic practices in neurology, paving the way for more precise and automated identification of neurological disorders.

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Publication Details

Journal
Technology and Health Care
Published
2024-12-16
DOI
https://doi.org/10.1177/09287329241291334
Citations
6
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
2.06
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article

Multi-modality NDE fusion using encoder–decoder networks for identify multiple neurological disorders from EEG signals

Rajeev Srivastava, Shraddha Jain
6 citations
Technology and Health Care
EEG and Brain-Computer Interfaces
2.06
article

Multi-modality NDE fusion using encoder–decoder networks for identify multiple neurological disorders from EEG signals

Rajeev Srivastava, Shraddha Jain
article en
6 citations

Abstract

This innovative approach represents a substantial advancement in the field of neurological disorder diagnosis. By integrating diverse NDE data with EEG signals through advanced neural network techniques, we have developed a method that enhances the accuracy and efficiency of diagnosing multiple neurological conditions. This fusion of multi-modality data has the potential to revolutionize current diagnostic practices in neurology, paving the way for more precise and automated identification of neurological disorders.

Technology and Health CareVol. 33(5)
Indian Institute of Technology BHU (IN), Banaras Hindu University (IN)
Openalex Percentile: Top 13%
EEG and Brain-Computer Interfaces
2.06
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