Analysis and Enhancement of Human Cognitive Control using Noninvasive Brain-Computer Interfaces

Cognitive control including attention and working memory are crucial to human daily life. Whether a civilian who walks across a street or a military service member who is responsible for navigating a mission, cognitive control is involved, entirely. This ability is subject to impairment. People with attention disorder are easily disposed to distraction and lacks the ability to maintain the focus to a task. Multiple treatment strategies have been suggested which most of them has been pharmaceutical. Evidently, the medical treatment has side effects for long-term use. Moreover, it has a risk of drug misuse. Another line of treatment is psychological therapy which is safe but not always effective. There is an emerging evidence that signifies the role of cognitive stimulating activities to improve neuroplasticity and treat neurodegenerative diseases. An alternative strategy to improve neuroplasticity is using Brain-Computer Interfaces (BCIs). A BCI, sometimes called a Brain-Machine Interface (BMI) refers to a unidirectional or bidirectional communication pathway between the brain and an external machine . BMIs utilizes mathematical and machine learning methods to tap into the central nervous system (CNS). The aim of present dissertation proposal is to investigate the possibility of using noninvasive mobile BCI to evaluate and enhance cognitive control while offering appropriate solutions and major contributions to these fields of work. Electroencephalography (EEG) signals as a convenient brain imaging technology is employed to capture real-time activities of the CNS. I investigated cognitive control and motor learning during imagined body movement. Also, I examined the neural pattern associated with visual selective attention in occlusion-free and occluded conditions. Further, working memory as an instrumental brain mechanism has been investigated and, a novel real-time EEG-based short-term memory evaluation and enhancement platform based on neurofeedback is developed and discussed.