Brain Machine Interface

Brain-machine interface (BMI) systems allow communication without movement. For example, many people with “locked-in syndrome” cannot exercise command and control in any way. This can lead to extreme dependence and social exclusion, in addition to the obvious dissatisfaction and discomfort from this situation.
BMIs may be invasive or non-invasive. Invasive BMIs require surgery to implant the necessary sensors, whereas non-invasive BMIs do not. Over 80% of BCIs are non-invasive systems that measure the electroencephalogram (EEG), which reflects the electrical activity associated with mental tasks Any BMI has four components : signal acquisition (e.g. getting information from the brain); signal processing (extracting information from the signals and translating it to messages or commands); devices and applications (such as a speller or robotic device); and an application interface (or operating environment) that determines how these components interact with each other and the user (neurofeedback training). The main intention of our research is on analytical and computational techniques for the investigation of brain effective and functional connectivity of cognitive functionality through data analysis, modeling, and integration. Indeed, the fast advance of multichannel data acquisition technology (fMRI, EEG, ECoG etc.) and processing capability has enhanced the development of novel approaches to the study of brain networks.