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EEG signals are interpreted, analyzed and classified by many researchers for use in brain–computer interfaces. Although there are many different EEG signal acquisition methods, one of the most interesting is motor imagery signals. Many different signal processing methods, machine learning and deep learning models have been developed for the classification of motor imagery signals. Among these, Convolutional Neural Network models generally achieve better results than other models. Because the size and shape of the data is important for training Convolutional Neural Network models and discovering the right relationships, researchers have designed and experimented with many different input shape structures. However, no study has been found in the literature evaluating the effect of different input shapes on model performance and accuracy. In this study, the effects of different input shapes on model performance and accuracy in the classification of EEG motor imagery signals were investigated, which had not been specifically studied before. In addition, signal preprocessing methods, which take a long time before classification, were not used; rather, two CNN models were developed for training and classification using raw data. Two different datasets, BCI Competition IV 2A and 2B, were used in classification processes. For different input shapes, 53.03–89.29% classification accuracy and 2–23 s epoch time were obtained for 2A dataset, 64.84–84.94% classification accuracy and 4–10 s epoch time were obtained for 2B dataset. This study showed that the input shape has a significant effect on the classification performance, and when the correct input shape is selected and the correct CNN architecture is developed, feature extraction and classification can be done well by the CNN architecture without any signal preprocessing. [ABSTRACT FROM AUTHOR]

• We introduce No-Filter EEG (NF-EEG) for multi class EEG motor imagery classification. • Model uses raw EEG data without any signal preprocessing methods to extract features. • Accuracy for two-class BCI-IV-2A dataset is 93.56% and 88.40% for BCI-IV-2B dataset. • Proposed model has superior performance to many state-of-the-art models. • NF-EEG is generalizable and robust with high accuracies and low standard deviations. 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 (NF-EEG) 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. [ABSTRACT FROM AUTHOR]