CNN-Based Image Analysis for EEG Signal Characterization

This article focuses on an attempt to classify and recognize the characterized images of EEG signals directly. For EEG signals, the recognition and judgment of different signals has been the key direction of research. CNN (Convolutional Neural Network) models are usually used for recognition of EEG raw signals about movement and Imagery Dataset. However, the images of EEG raw signals are basically unreadable for researchers, so characterization is a common tool. However, direct recognition of the characterized images is a relatively empty area in the existing research because it requires much higher machine performance than the traditional raw signal recognition. However, feeding the extracted feature images into a CNN and training them can be an efficient and intuitive response to the potential of EEG for brain mapping. The main goal of this research is to examine the discriminative capabilities of traditional visual and image neural networks for pictures described by EEG data. This is not typical in contemporary brain-computer interface research. The direct recognition of the described photos uses a lot of GPU (graphics computing unit) resources, but for the characterized images are easier for people to read than the original images. This work indicates the viability of direct research on defined pictures and increases the application scenario of EEG signals.