NeuroNetFusion: enhanced EEG abnormality classification via multi-network TF-IDF feature selection.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Machine Learning

Background:

• Electroencephalogram (EEG) signals present complex, nonlinear patterns challenging traditional classification methods.
• Existing EEG studies often rely on limited, uni-directional feature extraction, hindering comprehensive analysis.
• There is a need for advanced frameworks that capture multi-contextual dependencies within EEG data.

Purpose of the Study:

• To introduce NeuroNetFusion, a novel classification framework for EEG signals.
• To enhance bio-signal classification by integrating multi-directionally expressed cross-dependence information.
• To systematically integrate and select multi-context network-based features for improved EEG analysis.

Main Methods:

• EEG signal preprocessing using Savitzky-Golay (SG) filter and Discrete Wavelet Transform (DWT).