Nowadays brain connectivity plays a key role within the development of different neural diseases, but also in the normal working brain. Understanding brain connectivity is highly useful for understanding the normal processes of the brain and how changes in these processes may lead to a certain disease. However, most of the tools for EEG/MEG processing which are available nowadays do not provide functionalities for obtaining connectivity measures and those that provide these functionalities are not intuitive. Furthermore, only few of them allow visualizing this kind of information, but only by using matrix and graph representations. This is such an important problem not only for the research community but also for the clinicians, because it makes the interpretation of the results such a complex task. For this reason, this research line develops a new useful and efficient tool for the visualization of brain connectivity data, in a way that the end-user can visualize the important information in an interactive way. For this aim, we are following a user-centered design which involves the end-users during the whole process, considering their opinion and needs.
Electroencephalography is well-known for its importance in the diagnosis and treatment of different mental and neural disorders. This is due to its high temporal precision but also for being an inexpensive technology. However, the technology available nowadays is limited by several facts such as the limitation of the patient’s mobility due to the high amount of wires that keep the patient attached to the bed. This is an important issue since it makes the patient feel uncomfortable and hence, the EEG session might not represent the normal activity of the patient. On the other hand, this makes the work of the whole medical or research staff even more difficult, especially in patients which are not able to move easily or even worse, in young-age patients or those who are suffering a seizure. For this reason, with this research line, we decided to develop an application for using a wireless EEG in the monitorization of patients, in order to make the patient experience better and the staff work easier. For this purpose, we are following a user-centered design which involves the end-users during the whole process, considering their opinion and needs.
Development of novel methods for experimental study and control of stochastic processes in the human brain during visual perception (2016-2019)
Ministerio de Economía y Competitividad
PI: Alexander Pisarchik
Characterization and prediction of extreme events in neurophysiological brain activity (2018). UPM PI: Alexander Pisarchik
Predictability of catastrophic transitions in climate (2017). UPM PI: Alexander Pisarchik
Training neural networks to enhance memory and leaning capacity (2015-2016) Fundación para el Conocimiento Madri+d PI: Alexander Pisarchik
Organization of Seminar and Workshop “Multistability and Tipping: From Mathematics and Physics to Climate and Brain (2014-2016) Max Planck Institute for Physics and Complex Systems PIs: Alexander Pisarchik, Ulrike Feudel, Ken Showalter
FRAILBRAIN (Contrato FIBHUG)
Development of novel methods for experimental study and control of stochastic processes in the human brain during visual perception (NACIONAL) ; Methods of nonlinear dynamics and artificial intelligence for processing and analysis of extensive neurophysiological data (FED RUSA); Development of novel methods for the experimental study and control of nonlinear processes in the neural network of human brain during visual perception (FED RUSA).