The neuroengineering group focuses on advancing Brain-Machine Interface (BMI) technology. We are specifically interested in closed-loop neurofeedback systems in its interaction with the brain, thus including both systems engineering and neuroscience. 

In a closed-loop BMI, the brain is interfaced in real time to a machine (often a computer, BCI) through feedback that reflects ongoing brain activity (i.e. neurofeedback, NFB) [2]. In other words, through NFB, often displayed visually on a computer screen, BCI users can voluntarily control specific features of their own brain activity (i.e. neural self-control). This promotes a neural learning environment that stimulates cortical plasticity [3]. With this ability of inducing both short- and long-term modulations in the brain activity [3], BCIs have shown potential in restoring motor function following neurological trauma, such as a stroke, and in improving pathological symptoms in people with cognitive impairments, such as Attention-Deficit Hyperactivity Disorder (ADHD) [4], [5].


The neuroengineering team uses non-invasive recording techniques including electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS). We are interested in both the biological and machine-side of the BCI system as well as the dynamic interaction between the two. From the machine perspective, we are active within applied AI research to investigate how real-time adaptive AI can be implemented in BMIs to promote learning. From the biological perspective, we are interested in both how human learning of neural self-regulation can be improved and how BMIs can trigger brain plasticity in a controlled manner.

Elaine Åstrand, Associate Professor,Senior Lecturer

Room: U1-130
Phone: 021-103125