NF-EEG: A generalized CNN model for multi class EEG motor imagery classification without signal preprocessing for brain computer interfaces

Abstract

Objective: Brain Computer Interface (BCI) systems have been developed to identify and classify brain signals and integrate them into a control system. Even though many different methods and models have been developed for the brain signals classification, the majority of these studies have emerged as specialized models. In addition, preprocessing and signal preprocessing methods which are largely based on human knowledge and experience have been used extensively for classification models. These methods degrade the performance of real -time BCI systems and require great time and effort to design and implement the right method. Approach: In order to eliminate these disadvantages, we developed a generalized and robust CNN model called as No-Filter EEG (NFEEG) to classify multi class motor imagery brain signals with raw data and without applying any signal preprocessing methods. In an attempt to increase the speed and success of this developed model, input reshaping has been made and various data augmentation methods have been applied to the data. Main results: Compared to many other state-of-the-art models, NF-EEG outperformed leading state-of-the-art models in two most used motor imagery datasets and achieved 93.56% in the two-class BCI-IV-2A dataset and 88.40% in the two-class BCI-IV-2B dataset and 81.05% accuracy in the classification of four-class BCI-IV-2A dataset. Significance: This proposed method has emerged as a generalized model without signal preprocessing and it greatly reduces the time and effort required for preparation for classification, prevents human-induced errors on the data, presents very effective input reshaping, and also increases the classification accuracy.