Deep Learning Techniques for Brain Computer Interface: A Review

Brain-computer interfaces (BCIs) often face challenges concerning low recognition accuracy and increased calibration periods, especially when identifying motor imagery tasks for subjects with ambiguous characteristics and classifying intricate motion control tasks via electroencephalogram (EEG)-electromyogram (EMG) fusion analysis. In the past few decades, Brain-Computer Interfaces (BCI) have come to the attention of several researchers. In Motor Imagery (MI)-BCI, the central nervous system is directly connected to a computer or an automated system. Electromyographic (EMG) signal properties are utilised in motor imagery brain-computer interface (MI-BCI) systems. Multiple approaches have been proposed for collecting EEG signal characteristics in recent years. The incorporation of deep learning approaches into brain-computer interfaces (BCIs) has demonstrated considerable potential in improving the interpretation of neural signals for many applications. This method utilizes sophisticated algorithms to analyze complex brain activity, thereby enhancing device control, including robotic exoskeletons, and deepening the understanding of cerebral dynamics. This review paper discusses brain-computer interfaces (BCIs) that utilize deep learning, highlighting the significance of BCIs in conjunction with this technology. It presents a comprehensive survey of the literature, encompassing over 10 papers. It also outlines a comparative analysis based on parameters such as research objectives and goals, dataset utilization, methods, and algorithms used to address BCI challenges, as well as limitations and future work. Additionally, this paper discusses current research trends in BCI using deep learning.