Adaptive Optimization of Transfer Learning in Cross-sample Electroencephalogram Depression Prediction Models

DOI: https://doi.org/10.62517/jike.202504413

Author(s)

Zifei Song

Affiliation(s)

School of Canada Yantai Secondary School, Yantai, China *Corresponding Author

Abstract

This paper focuses on the adaptive optimization problem of transfer learning in cross-sample electroencephalogram depression prediction models. This paper expounds the significance of electroencephalogram (EEG) signals in depression prediction and the challenges faced in cross-sample prediction, and analyzes the basic concepts and common methods of transfer learning and its preliminary application in the medical field. This paper explores the influence of factors such as data differences and individual differences on the model in cross-sample electroencephalogram (EEG) depression prediction, and elaborates in detail the adaptive optimization strategies of transfer learning in data preprocessing, feature extraction, model training and adjustment, etc. It points out the current challenges and looks forward to the future development direction, aiming to provide theoretical support for constructing a more accurate and universal cross-sample electroencephalogram depression prediction model.

Keywords

Transfer Learning; Cross-sample; Electroencephalogram (EEG) Depression Prediction Model; Adaptive Optimization

References

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