Neurofeedback BCI based on complexity of the EEG signal for treatment of cognitive disorders

Cognitive disorders represent a complex category of conditions that introduce difficulties in the performance of brain processes for an individual. These disorders significantly impact quality of life by requiring external support and specific therapies. Nowadays, there are various treatments, but they involve undesirable side effects that drastically affect the individual's life. In recent years, new technologies have taken hold: Brain Computer Interfaces (BCI). BCIs are systems that track brain activity in real time and create direct communication between the brain and an external device. Neurofeedback (NFB), in particular, is a type of biofeedback that allows the subject to modulate brain waves by enhancing related activities. The aim of this thesis is to develop a closed-loop passive auditory NFB using electroencephalography (EEG). The idea is to track, in real time, brain activity by generating a sound proportional to it in the form of coloured noise. This will be possible through the introduction of a new complexity index: the PLE. For this purpose, the steps for creating the loop using the Robot Operating System (ROS) programming environment will be illustrated. Following the development of the code for sound generation, EEG signals will be acquired from various subjects. Finally, the recordings of these signals will be exploited for offline analysis to investigate the effect of sound on brain dynamics.