Channel selection and feature extraction on deep EEG classification using metaheuristic and Welch PSD

Brain computer interfaces are important for different application domain such as medical, natural interfaces and entertainment. Besides the difficulty of gathering data from the human brain via different channel probs, preprocessing of data is another different and important task that must be solved in order to get better achievement. Selection of the most active channels is an important problem to achieve high classification accuracy. Metaheuristics are good solutions for selecting the optimal subset from the original set, as they have the ability to obtain an acceptable solution in a reasonable time. At the same time, it is necessary to use the correct feature extraction method so that the data can be properly represented. In addition, traditional deep learning methods used for emotion recognition ignore the spatial properties of EEG signals. This reduces the classification accuracy. In this study, we used artificial bee colony optimization algorithm on the seed dataset to increase the classification accuracy. We implemented and tested four different variations of this algorithm. Then, we extracted the features of the obtained channels with the Welch PSD method. We used enhanced capsule network as a machine learning algorithm and showed the best configuration to solve the problem. At the end of the process, 99.98% training and 99.83% test accuracy rates were obtained.