Professor Vinod Achutavarrier Prasad | Singapore Institute of Technology; Dr Yao Fengyuan | Institute of Mental Health; Dr Magadi Gopalakrishna Harish | Institute of Mental Health
Competition Sponsor: Ministry of Health and National Research Foundation of Singapore
Awardee year: 2023
Mild Cognitive Impairment (MCI) has been identified as an early sign of more severe neurological disease and is considered a prodromal state to clinical Alzheimer’s disease. The main symptoms of MCI are loss of short-term memory, attentional processing and visual/spatial perception. There is presently no proven pharmacological treatment for MCI. Hence, there is increasing interest in the use of Brain Computer Interface (BCI)-based Neurofeedback Training (NFT) that could potentially regulate brain activity underlying the symptoms of MCI, targeting slowing down the deterioration of cognitive functions. BCI-based NFT utilizes operative conditioning principles by selectively enhancing or suppressing the frequency, location, amplitude, or duration of a specific brain activity which allows subjects to maintain their brain state in a desirable condition and to improve their cognitive function through training. This project will develop computer games controlled by a non-invasive wireless Electroencephalograph-based BCI for MCI patients that will help to slow down the deterioration of their cognitive capabilities of sustained attention, short-term memory and visuospatial attention, by fine-tuning their respective neural correlates. We will investigate Error-related Potentials (ErrP) evoked in short-term memory and visuospatial attention (VSA)-based target selection task, and quantify the modulations in ErrP caused by the severity of the error, which in turn help to develop subject-specific NFT interventions. The project will develop a computer-based virtual system mimicking daily life scenarios that can be controlled by focussed attention, short-term memory and VSA using BCI, assess the impact of the training sessions on MCI patients, and recommend optimized subject-specific NFT interventions.