Multimedia Knowledge and Social Media Analytics Laboratory

Brain Research and BCI

Description:

The use of electroencephalograms (EEG) for conducting Brain Research has been one of the most exciting research fields gaining more and more attention. The potential to tap into the human’s brain and understand its secrets carries the promise of improving our lives in many different ways. With the aim to fulfill this promise, MKLab has setup a Lab with highly sophisticated equipment (i.e. ranging from a high-density EEG system (EGI 300 Geodesic EEG system – GES300) and the SMI Red500 eye tracker with high refresh rate, all the way to the lightweight EEG devices of Emotiv Epoc+ and Emotiv Insight) in order to perform research along two related fronts:

Brain Computer Interfaces:

Brain-computer interfaces (BCIs) have been gaining momentum in making human-computer interaction more natural, especially for people with neuro-muscular disabilities. Among the existing solutions the systems relying on electroencephalograms (EEG) occupy the most prominent place due to their non-invasiveness. However, the process of translating EEG signals into computer commands is far from trivial, since it requires the optimization of many different parameters that need to be tuned jointly. Towards this end, MKLab has developed the following expertise in BCIs:

  • Steady-state-visual-evoked-potentials for interacting with the computer through visual stimuli
  • Event-related potentials measuring the brain response as a direct result of a specific sensory, cognitive, or motor event.
  • Error-related potentials for understanding when the undertaken command is different from the original intention.
  • Combination of EEG signals with eye-tracking towards the developments of multi-modal interfaces.

Brain Cognitive Processes:

Together with an interdisciplinary team of researchers with complementary skills, MKLab employed the aforementioned equipment so as to perform research on brain cognitive processes. In particular, our goal is to advance the state of the art in vector field tomography (VFT), by exploiting the new methodology in 2D and extending its theory to 3D. Subsequently, we apply 3D-VFT to high density EEG data to solve the inverse EEG problem and determine the active states of the brain. Thus, by investigating brain activation in different experimental scenarios and among different experimental groups, we contribute in studying the human brain and understanding its cognitive processes. More specifically, differences concerning the cognitive abilities between healthy, MCI and AD patients, as well as men and women, are studied under different experimental protocols. The findings are interesting concerning not only research, but also serving for patient assessment and for the development of personalized "brain fitness" programs for each subject.

Key Publications: 

Vangelis P. Oikonomou, Georgios Liaros, Kostantinos Georgiadis, Elisavet Chatzilari, Katerina Adam, Spiros Nikolopoulos and Ioannis Kompatsiaris, Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs, Technical Report - eprint arXiv:1602.00904, February 2016

A. Tsolaki, D. Kazis, I. Kompatsiaris, V. Kosmidou, M. Tsolaki, M. Electroencephalogram and Alzheimer’s Disease: Clinical and Research Approaches.International Journal of Alzheimer’s Disease, 2014

A. Tsolaki, V. Kosmidou, L. Hadjileontiadis, I. Kompatsiaris, M. Tsolaki, Brain source localization of MMN, P300 and N400: Aging and gender differences.Journal of Brain research,  Elsevier, 2015.

C. Papadaniil, L. Hadjileontiadis, "Tomographic Reconstruction of 3-D Irrotational Vector Fields via a Discretized Ray Transform", Journal of Mathematical Imaging and Vision, 2015, DOI: 10.1007/s10851-015-0559-y.

C. Papadaniil, V. Kosmidou, A. Tsolaki, L. Hadjileontiadis, M. Tsolaki, I. Kompatsiaris, Age Effect in Human Brain Responses to Emotion Arousing Images: The EEG 3D-Vector Field Tomography Modeling Approach, IEEE Trans on Autonomous Mental Development, special issue: Multimodal Modeling & Analysis Informed by Brain Imaging, vol pp(99), 2015