Mental workload or cognitive load can be stated as how much mental resources are required to complete a task. High mental workload for a prolonged period can lead to mental fatigue, poor decision making, mental illness, etc. That shows the importance of monitoring mental workload. Electroencephalography (EEG) signals have been used to measure the brain activity. The dataset used in this paper is of mental workload during arithmetic task and based on number of tasks performed; subjects were classified into good counter and bad counter (Zyma et al. in Data 4:14, 2019). In this work, convolution neural network (CNN) model called as EEGNet is utilized for classification of mental state. In original dataset, average classification accuracy was 61.78% ± 0.076, to improve the accuracy data augmentation through Synthetic Minority Oversampling Technique (SMOTE) is used. After data augmentation, average classification accuracy reached to 99.56% ± 0.005.