New proposals for EEG and fMRI based Brain Computer Interface technology

Abstract

In this manuscript three related aspects of research on BCI systems were discussed. These aspects were the evaluation of a nonlinear feature extraction algorithm for BCI, the analysis of the functional connectivity between the signals acquired in different brain regions when a user performs an operant conditioning paradigm with fMRI based BCI technology, and the development of BCIs applications for disabled subjects. We have introduced a new EEG signals features extraction techniques based on nonlinear time series analysis. This signal processing approach was tested offline considering three sessions of imaginary motor tasks. The main objective is increasing the performance of BCI systems extracting a more robust feature. In order to reach this objective a fast algorithm that computes the largest Lyapunov exponent, the DivA [7], was used. This implementation results to be computationally less onerous than the conventional ones, since it is not based on the time-delay embedding concept and also no intermediate computational steps are needed to obtain the final result. For this reason the DivA is particularly suitable for real time analysis, thus for BCI applications. Our evaluations underline the capability and the potentiality of this method in respect to the classical approach. The idea for future works is to integrate the nonlinear algorithm investigated in this thesis in a BCI system, thus using it on line. The design of a BCI based on our nonlinear feature extraction method could improve the performance of the systems that use sensory motor rhythms as neurophysiologic signals. The analysis of the functional connectivity between brain regions involved in the perception of pain is the second topic that were dealt with in this thesis. Thanks to the collaboration with the central institute of mental health, Heidelberg university in Mannheim, the dataset recorded with an fMRI based BCI technology have been analysed. The results reveal the possibility for a person to modulate the brain waves, in particular the neurophysiologic signals related to the perception of pain. Control over the pain modulatory system is an important target because it could enable a unique mechanism for clinical control over pain. Here, we found that using real-time functional MRI based BCI to guide training, subjects were able to learn to control activation both in anterior cingulated cortex and in the posterior insula. The BCI techniques could have an important role for treating disease, for example for the chronic pain treatment. An aspect that can be investigated in future work is the involvement of the medial cingulate cortex in the pain perception. Indeed when the subject deliberately induced increases or decreases in ACC or pIns fMRI activation, there was a corresponding change in the connection between the MCC and the other ROIs. In particular a more strong connection between MCC and pInsR can be noticed. In future works could be interesting to analyze the role of MCC in the perception of pain. Finally, we proposed the designs of different EEG based BCI applications. We aim to provide a significant quality of life improvement to users with severe disabilities. All the applications designed have been tested for able-bodied users, the future idea is to test the applicability of such tools also for the locked-in patients.