T. Tsagaroulis and A. Ben Hamza (Canada)
Multivariate quality control, EEG data, PCA
The use of multivariate statistical process control is facilitated by the proliferation of sensor data that is typically complex, high-dimensional and generally correlated. Electroencephalographs (EEGs) consist of vast amounts of complex data that require a trained professional to perform a proper analysis. In this paper, we present a user-friendly graphical interface to analyze EEG data using multivariate statistical dimensionality reduction techniques. Our experimental results show the effectiveness of our proposed approach in EEG data analysis.
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