Cognitive Load Recognition Using Multi-channel Complex Network Method

Modeling the cognitive events of human beings is an interesting task, but finding effective representations from electroencephalogram (EEG) data is one of the challenges. Recently, complex network analysis has gained considerable attention in the time series analysis, but most of the analysis is devoted to investigating single time series or just the time domain statistical features. Herein, we propose a novel approach using the frequency domain features to construct connections between different EEG channels to generate a multi-channel network. First, we transform the EEG time series to a frequency domain feature using the spectrogram of three frequency bands. Next, we generate a multi-channel network using the space distance and the classification is based on the network structural features. The results indicate that the proposed method gets good performance and is more efficient than the deep learning method to some degrees.

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