Dynamic optimization of mental workload in fNIRS-BCI system for cognitive rehabilitation

Cognitive rehabilitation has been proposed as an alternative treatment for Alzheimer's disease (AD) as it helps to preserve brain functionality. However, gains of cognitive training or rehabilitation may be eliminated due to cognitive overload and mental fatigue. This paper reports the development of a functional near-infrared spectroscopy (fNIRS) - brain-computer interface (BCI) that can adjust task difficulty adaptively. The aim is to have participants trained at their optimal level of difficulty and workload to maximize their gains. One patient with mild AD and one healthy control were recruited to test the functionality of proposed fNIRS-BCI system. The fNIRS-BCI system is able to process fNIRS signals in real time and adjust task difficulty accordingly. The healthy control was able to proceed to higher task levels, as compared to the mild AD patient. The fNIRS-BCI system has the potential as a tool to examine the efficacy of cognitive rehabilitation as an alternative treatment for AD.