Review on Signal Generation for Neurofeedback

Neurofeedback (NF) is a scientific method that alters the brain states to improve mental disorders. Neurofeedback can perform through Brain-Computer Interface (BCI) which involves hardware, and software to communicate with the outside environment using the brain's thoughts. Coronavirus disease (COVID-19) has shown a substantial influence on mental health symptoms because individuals are working from home (WFH). However, A brain condition known as Mental Fatigue (MF) is induced by continuous cognitive work and lowers mental attentiveness as well as negatively affects performance. There are different approaches to address different mental states and Neurofeedback strategies to change mental states. Thus, Neurofeedback can be an Intervention technique to reduce mental fatigue and improve cognitive task performance. Furthermore, it is proven by researchers that Machine Learning Technique can successfully detect Mental Fatigue through electroencephalography (EEG). Currently, there is no BCI that integrated Mental Fatigue detection and applies Neurofeedback strategies to reduce Mental Fatigue. This review identified a neurofeedback process that includes signal acquisition, signal pre-processing, feature extraction, classification and generation of feedback signals. This review aims to develop a general architecture of mental fatigue intervention through BCI.