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CROSS-SAMPLING RATE TRANSFER LEARNING FOR ENHANCED RAW EEG DEEP LEARNING CLASSIFIER PERFORMANCE IN MAJOR DEPRESSIVE
Charles A Ellis1, Robyn L Miller1, Vince D Calhoun1
1Tri-institutional Center for Translational Research in Neuroimaging and Data Science at Georgia State University, Emory University, and Georgia Institute of Technology.
Transfer learning improves electroencephalography (EEG) analysis for major depressive disorder (MDD) diagnosis. This method enhances model accuracy and robustness, even when using data with different sampling rates.
Area of Science:
- Neuroscience
- Machine Learning
- Biomedical Engineering
Background:
- Deep learning models require large datasets for robust performance.
- Electroencephalography (EEG) data is often limited in size, posing challenges for model development.
- Transfer learning is a promising technique to overcome data limitations in deep learning.
Conclusions:
- Early convolutional layers in deep learning models capture generalizable EEG representations.
- Transfer learning is a viable and effective strategy for enhancing deep learning models in EEG analysis, particularly for conditions like MDD.
- This study provides guidance for applying transfer learning to improve EEG-based diagnostic models, addressing challenges with small datasets and varying sampling rates.
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