Performance Comparison of Different EEG Analysis Techniques Based on Deep Learning Approaches

Deep learning models using neural networks are capable of finding hidden patterns and their associations without assistance of domain experts. Brain-computer interface (BCI) is one of the popular research areas and has seen a variety of research approaches in recent years. Combining BCI research with deep learning has opened doors to many interdisciplinary applications in the aviation industry, passenger safety and many more. Electroencephalogram (EEG) analysis is a crucial part in the research of BCI. This paper aims to present a survey of published works on EEG analysis techniques in the past few years. The paper gives the different deep learning techniques used in the analysis of EEG signals. In this paper, we propose a brief study and comparison of existing methodologies for EEG signal analysis using deep learning models.