Cognitive Neuroscience (CNS) is the scientific field that is concerned with the study of the biological processes and aspects that underlie cognition, with a specific focus on the neural connections in the brain involved in mental processes. It addresses the questions of how cognitive activities are affected or controlled by neural circuits in the brain. CNS uses the experimental methods of cognitive psychology and artificial intelligence to create and test models of higher-level cognition such as thought and language. It maps higher-level cognitive functions to known brain architectures and known modes of neuronal processing.
Our research is highly interdisciplinary and specialized in the big field of CNS but also covers further disciplines like cognitive and behavioral psychology, neuropsychology, signal processing, brain-computer interfaces, neurophysiology, biomedical engineering and affective neuroscience.
By using different neuroimaging methods like electroencephalography (EEG), functional magnetic resonance imaging (fMRI) and functional near infrared spectroscopy (fNIRS) we investigate brain activity and networks of higher-level cognition in different modalities.
Historically, a Brain-Computer Interface (BCI) is a system that measures central nervous system (CNS) activity and converts it into artificial output that replaces, restores, enhances, supplements, or improves natural CNS output (Wolpaw et al., 2012). In past years, BCI applications have mainly been developed for severely disabled persons providing new channels of communication and body control but in recent years BCI research have become more and more interesting for a broader community of researchers. Especially the type of BCIs, which are not consciously controlled by the user or reacting to external stimulation, called passive BCIs. These systems derive its outputs from brain activity, in order to enrich human–machine interaction with implicit information on the actual state of the user. Having access to the user’s ongoing brain activity enables applications spanning a variety of domains such as brain-activity based gaming, workload assessment, brain activity-based biometrics and neuromarketing or neuroergonomics. Currently our research focusing on passive BCI systems for healthy users with two special applications. First the investigation of unconscious like/dislike decision making and second the development of a neuro-adaptive learning environment which takes the mental state of the user into account. This brain inspired learning framework will improve language learning considering fatigue and mental workload of the user through linking BCI and MR technology.
This project is in collaboration with the Institute of Computer Graphics and Vision, TU Graz.
Motor Imagery (MI) is one task which has been used for driving brain plasticity and motor learning in several fields including sports, motor rehabilitation and brain-computer interface (BCI) research. A BCI is a device translating brain signals into control signals providing severely motor-impaired persons with an additional, non-muscular channel for communication and control. In the past many studies have shown that brain activity changes associated with MI can serve as useful control signals for BCIs. By using more vivid and engaging MI tasks instead of simple hand/finger tapping tasks, the performance of a BCI can be improved. In several imaging studies we found stronger and more distinctive brain activity in a broader network of brain areas. For example, imagining a complex action requires not only motor-related processing but also visuo-spatial imagery including a fronto-parietal network. The neural activity in MI of reach-to-grasp movements depends on the type of grasping which recruits a network including posterior parietal and premotor regions. Furthermore we found increased activation in parietal and frontal regions during imagery of emotion-laden objects and sports activities. Our results indicate that visuo-spatial cognition and action affordances play a significant role in MI eliciting distinctive brain patterns and suggested to improve the performance of future BCI systems. To support these first findings further research focusing on (sports)motor imagery and its neural correlates is still ongoing.
EEG-neurofeedback is a method to self-regulate one’s own brain activity to directly alter the underlying neural mechanisms of cognition and behavior. People can self-control some of their brain functions in real-time. This method is currently used for many different applications, including e.g. experiments (to deduce the role of cognition and behaviour on specifc neural events), peak-performance training (to enhance the cognitive performance in healthy subjects) and therapy (to help people normalize their deviating brain activity or help physically disadvantaged people restore motor functions). Simple 2D EEG-neurofeedback has already been used for quite some time now, while 3D EEG-neurofeedback in the form of virtual reality gained fame and interest over the last few years. While a vast amount of 2D methods and paradigms have already been tested and validated, the 3D counterpart still holds high potential for new possible treatment methods. This project aims to develop and test novel 3D neurofeedback visualizations including VR environments. This project is in collaboration with the Game Lab Graz, Institute of Interactive Systems and Data Science.
Luis Alberto Barradas Chacon, M.Sc.
Institute of Neural Engineering
8010 Graz, Austria