ArticleFrontiers in neuroinformatics2022
A Systematic Approach for Explaining Time and Frequency Features Extracted by Convolutional Neural Networks From Raw Electroencephalography Data.
Article in Frontiers in neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Development of a deep learning-based prediction model for postoperative delirium using intraoperative electroencephalogram in adults.NPJ digital medicine · 2025Article
- A nonoscillatory, millisecond-scale embedding of brain state provides insight into behavior.Nature neuroscience · 2024Article
- Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures.bioRxiv : the preprint server for biology · 2024Article
- Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics.Neuroimage. Reports · 2023Article
- Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data.bioRxiv : the preprint server for biology · 2023Article
- A non-oscillatory, millisecond-scale embedding of brain state provides insight into behavior.bioRxiv : the preprint server for biology · 2023Article
- NOVEL APPROACH EXPLAINS SPATIO-SPECTRAL INTERACTIONS IN RAW ELECTROENCEPHALOGRAM DEEP LEARNING CLASSIFIERS.bioRxiv : the preprint server for biology · 2023Article
- Towards greater neuroimaging classification transparency via the integration of explainability methods and confidence estimation approaches.Informatics in medicine unlocked · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In recent years, the use of convolutional neural networks (CNNs) for raw resting-state electroencephalography (EEG) analysis has grown increasingly common. However, relative to earlier machine learning and deep learning methods with manually extracted features, CNNs for raw EEG analysis present unique problems for explainability. As such, a growing group of methods have been developed that provide insight into the spectral features learned by CNNs. However, spectral power is not the only important form of information within EEG, and the capacity to understand the roles of specific multispectral waveforms identified by CNNs could be very helpful. In this study, we present a novel model visualization-based approach that adapts the traditional CNN architecture to increase interpretability and combines that inherent interpretability with a systematic evaluation of the model
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.