| dc.description.abstract | Electroencephalogram (EEG) analysis plays a
crucial role in understanding brain activity and diagnosing
neurological conditions. Traditional methods often
struggle with the complexity and high dimensionality of
EEG data. This study addresses these challenges by
developing a novel framework that leverages generative
self-supervised learning and autoencoder architecture to
enhance EEG data analysis. The primary problem lies in
the accurate and efficient extraction of meaningful features
from EEG signals, which are inherently noisy and complex.
The objectives of this research are to improve feature
extraction from EEG data using an autoencoder and
accurately predict sleep stages using advanced machine
learning techniques. The methodology involves pre-
processing the EEG data, segmenting it into 30-second
epochs, and annotating it according to standard scoring
guidelines. An autoencoder is used for feature extraction,
followed by the application of Synthetic Minority Over-
sampling Technique to address the class imbalance. The
encoded features are then classified using a robust machine
learning model within a TensorFlow environment. Results
demonstrate a high average F1-score of 0.97, indicating the
effectiveness of the proposed framework. High evaluation
metrics, such as Area Under the Curve, Cohen's Kappa
Coefficient, and Matthews Correlation Coefficient, further
validate the model's performance. This research presents
an effective framework for EEG data analysis, combining
generative self-supervised learning and autoencoder
techniques. Future work will focus on enhancing the
autoencoder architecture, and applying transfer learning
to diverse datasets. | en_US |
