Comparative analysis of VEP signals discrimination methods based on time-frequency transformation and CNN-2D

Abstract

The Visual Evoked Potential (VEP) examination is used to analyze the appropriate functioning of the optical pathways from the retina to the visual cortex. In hospitals, the diagnosis made by physicians is based mainly on reading the temporal trace and identifying the latency P100. However, after a considerable research effort, it has been confirmed that this method is subjective and relatively less reliable. In our work, we report different approaches to resolve the inadequacy of traditional classification, by studying the efficiency of VEP signal classification in a comparative approach using 3 models: Model A: STFT-CNN, Model B: CWT-CNN, and Model C: Wigner-Ville-CNN, therefore we evaluate in the same context the effectiveness of using a pre-trained 2D CNN structure. The time-frequency transformation allows us to generate two-dimensional data from one-dimensional signals to bring out the integrated features that are not valued in the temporal plot, and then exploit them for good discrimination between the two classes, in order to be able to use a CNN-2D classification architecture, taking into consideration the advantages offered by this architecture in terms of the involvement of the attribute extraction phase and its efficiency in classifying 2D data. The results provided by the different scenarios proved that the Wigner-Ville transformation combined with a pre-trained CNN architecture can be considered a good method in terms of different performance metrics, which demonstrates that it is a successful candidate for providing significant assistance to physicians in their analysis of VEP signals.