BCI-based real-time processing for implementing deep learning frameworks using motor imagery paradigms

Abstract

As a cognitive process, motor imagery (MI) includes mentally simulating motor actions in the absence of physical movement. It has a variety of uses, including assistive technologies, medical diagnostics, and rehabilitation. MI paradigms are utilized in conjunction with brain-computer interfaces (BCI), which use electroencephalographic recordings (EEG) because of their high temporal resolution, cheap cost, portability, and non-invasiveness. BCIs apply MI paradigms by directly connecting the human brain to a computer. However, because scalp readings are non-stationary and non-linear, real-time processing of EEG signals is challenging. Furthermore, in order to minimize the impact of outside noise and artifacts, clinical MI methods must be implemented under carefully monitored laboratory conditions. A deep learning model-based approach is shown for analyzing EEG data and giving real-time feedback to a brain-computer interface. Generally, the system's design is portable and low-cost, allowing the MI paradigm to perform under poorly regulated sampling conditions.