Primary Investigator: mgr Marcin Koculak

Research project objectives/Research hypothesisMain objective of the project is to investigate neuronal markers of consciousness and to utilize them forautomatic classification of level of consciousness. It is known that brain activity associated withconsciousness is not limited to only one brain region or one characteristic of neuronal activity. To fullyunderstand this phenomenon, it is necessary to find measures that reflect complex functional characteristicsof underlying brain processes. Therefore, the main focus of the project is to trace those unique neuronalcharacteristics of consciousness with advanced signal processing and machine learning methods.Proper choice of methods used successfully in brain-computer interfaces will enrich our knowledge aboutneuronal mechanisms underlying the consciousness and supply means for constructing new diagnosticmethods e.g. for patients with disorders of consciousness.Research project methodologyAcquisition of diversified electroencephalographic (EEG) signal will be possible through experimentsinvolving healthy participants (awake and asleep). Neuronal activity will be measured by means of EEG,which is a non-invasive and easy to use technique, characterized by a very good temporal resolution andlow exploitation cost.Project is divided into three separate stages, during which we will analyze resting state brain activity underdifferent levels of consciousness (e.g. awake, drowsy, in deep sleep) as well as brain responses to externalsensory stimulation. Next stage will focus on automatic classification of state of consciousness based onfindings from previous stages. This will help develop protocols for improving diagnosis of e.g. patientswith disorders of consciousness (e.g. in vegetative state or minimally conscious state).Auditory stimulation, tactile stimulation and the combination of both will be used to acquire reliable brainresponses that will differentiate between different levels of consciousness. We will test novel paradigmsthat successfully employed in BCI, e.g. code-modulated stimuli or features based on spatio-temporalaspects of stimulation.Signal analysis and classification will be focused on utilizing novel approaches, especially those based oninformation geometry, which seem very promising for handling complex biomedical signals. This will besupported by advanced machine learning algorithms e.g. deep recurrent neural networks (dRNN). Thiswork will be based in experience gathered during an internship at RIKEN in Japan and will continuedthrough collaboration with Tomasz Rutkowski, Ph.D.Expected impact of the research project on the development of science, civilization and societyThe principal effect of the project will be broadening the knowledge about neural correlatesof consciousness. It will also allow us to evaluate the usefulness of new techniques for studyingmechanisms underlying the emergence of consciousness. Subsequently, we will evaluate novel auditoryand tactile stimulation methods and innovative data analysis algorithms, which will later be a crucial factorfor transferring acquired knowledge into real life solutions.The project results will also have a direct impact on consciousness diagnosis, especially in the context of itsdisorders. Automatic classification of level of consciousness could later be used to monitor patients, whichcan be crucial for individuals in minimally conscious state. New methods of stimulation and data analysismight lead to development of new BCI based communication protocols for patients or help to improve theones that already exist.