Electrical and Computer Engineering
Neuroengineering
The neuroengineering team 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.
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In a closed-loop Brain-Machine Interface (BMI), the brain is interfaced in real time to a machine (often a computer, called BCI) through feedback that reflects ongoing brain activity (i.e. neurofeedback, NFB) [1]. 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-regulation). This promotes a neural learning environment that stimulates cortical plasticity [2]. As such, BCIs have been shown to induce both short- and long-term modulations in the brain activity [2]. However, for BCIs to be interesting from a therapeutic aspect, these activity modulations need to translate into behavioral improvements. In this respect, several BCI studies show promise in restoring motor function following neurological trauma, such as a stroke [3], and in improving pathological symptoms in people with cognitive impairments, such as Attention-Deficit Hyperactivity Disorder (ADHD) [4].
The neuroengineering team uses non-invasive recording techniques including eye-tracking, electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) to advance BCI technology and study closed-loop interaction effects. 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 apply advanced signal processing and AI to investigate how information encoded in the brain activity can be extracted in real time. 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.
More information on team activities.
External link.
References:
[1] R. Sitaram et al., ‘Closed-loop brain training: the science of neurofeedback’, Nat. Rev. Neurosci., vol. 18, no. 2, pp. 86–100, 2017
[2] H. J. Engelbregt et al., ‘Short and long-term effects of sham-controlled prefrontal EEG-neurofeedback training in healthy subjects’, Clin Neurophysiol, vol. 127, no. 4, pp. 1931–1937, Apr. 2016
[3] M. A. Cervera et al., ‘Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis’, Annals of Clinical and Translational Neurology, vol. 5, no. 5, pp. 651–663, 2018
[4] M. Arns, C. K. Conners, and H. C. Kraemer, ‘A decade of EEG Theta/Beta Ratio Research in ADHD: a meta-analysis’, J Atten Disord, vol. 17, no. 5, pp. 374–383, Jul. 2013