Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode...

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Attention-deficit/hyperactivity disorder (ADHD) treatment often involves neurofeedback systems.
• Accurate measurement of human attention levels is crucial for effective neurofeedback.
• Existing methods for attention measurement using electroencephalography (EEG) have limitations.

Purpose of the Study:

• To propose and validate a novel optimized complex network method (OCNM) for measuring attention levels.
• To utilize single-electrode EEG signals for attention assessment.
• To compare the performance of OCNM against established attention measurement techniques.

Main Methods:

• Reconstructed EEG data epochs into network nodes using a time-delay embedding algorithm.
• Defined network edges based on Euclidean distances between nodes.
• Optimized OCNM parameters (delaying time, embedding dimension, connection threshold) individually.
• Extracted network features (average degree, clustering coefficient) for classification using an LDA classifier.

Main Results:

• The OCNM achieved the highest classification accuracy rate of 80.67%.
• OCNM outperformed the attention meter method (70.58%) and the α+β+δ+θ+R method (68.88%).
• The method demonstrated effective classification of concentration and relaxation states.

Conclusions:

• The proposed OCNM is a promising method for measuring attention levels using single-electrode EEG.
• OCNM has the potential for integration into EEG-based neurofeedback systems for ADHD treatment.
• This approach offers a more accurate and potentially simpler method for attention monitoring.