Convolutional Neural Network and Adversarial Autoencoder in EEG images classification
This addresses EEG classification for neuroscience applications, but it appears incremental as it combines existing methods without clear breakthroughs.
The paper tackled EEG-based classification of hand movement activities by converting raw EEG signals into 2D topograms and applying supervised and semi-supervised neural networks, but no concrete results or numbers were reported.
In this paper, we consider applying computer vision algorithms for the classification problem one faces in neuroscience during EEG data analysis. Our approach is to apply a combination of computer vision and neural network methods to solve human brain activity classification problems during hand movement. We pre-processed raw EEG signals and generated 2D EEG topograms. Later, we developed supervised and semi-supervised neural networks to classify different motor cortex activities.