Noninvasive Brain Computer Interfaces (BCI) have been promoted to be used for neuroprosthetics. However, reports on applications with electroencephalography (EEG) show a demand for a better accuracy and stability. Here we investigate whether near-infrared spectroscopy (NIRS) can be used to enhance the EEG approach. In our study both methods were applied simultaneously in a real-time Sensory Motor Rhythm (SMR)-based BCI paradigm, involving executed movements as well as motor imagery. We tested how the classification of NIRS data can complement ongoing real-time EEG classification. Our results show that simultaneous measurements of NIRS and EEG can significantly improve the classification accuracy of motor imagery in over 90% of considered subjects and increases performance by 5% on average (p<0:01). However, the long time delay of the hemodynamic response may hinder an overall increase of bit-rates. Furthermore we find that EEG and NIRS complement each other in terms of information content and are thus a viable multimodal imaging technique, suitable for BCI.

Recently, spatio-temporal filtering to enhance decoding for Brain-Computer-Interfacing (BCI) has become increasingly popular. In this work, we discuss a novel, fully Bayesian–and thereby probabilistic–framework, called Bayesian Spatio-Spectral Filter Optimization (BSSFO) and apply it to a large data set of 80 non-invasive EEG-based BCI experiments. Across the full frequency range, the BSSFO framework allows to analyze which spatio-spectral parameters are common and which ones differ across the subject population. As expected, large variability of brain rhythms is observed between subjects. We have clustered subjects according to similarities in their corresponding spectral characteristics from the BSSFO model, which is found to reflect their BCI performances well. In BCI, a considerable percentage of subjects is unable to use a BCI for communication, due to their missing ability to modulate their brain rhythms–a phenomenon sometimes denoted as BCI-illiteracy or inability. Predicting individual subjects’ performance preceding the actual, time-consuming BCI-experiment enhances the usage of BCIs, e.g., by detecting users with BCI inability. This work additionally contributes by using the novel BSSFO method to predict the BCI-performance using only 2 minutes and 3 channels of resting-state EEG data recorded before the actual BCI-experiment. Specifically, by grouping the individual frequency characteristics we have nicely classified them into the subject ‘prototypes’ (like μ - or β -rhythm type subjects) or users without ability to communicate with a BCI, and then by further building a linear regression model based on the grouping we could predict subjects' performance with the maximum correlation coefficient of 0.581 with the performance later seen in the actual BCI session.

The cerebellum plays an important role in pain processing but its function in headache and specifically in migraine is not known. We therefore compared 54 migraineurs with pairwise matched healthy controls in a magnetic resonance imaging study on neuronal cerebellar activity in response to nociceptive trigeminal sensation and also investigated possible structural alterations. Headache frequency, disease duration, and the proximity to a migraine attack were used as co-factors. Migraine patients showed functional and structural alterations in the posterior part of the cerebellum, namely crus I and crus II. Gray matter volume changes were seen on the right side whereas functional changes were ipsilateral to the stimulation, on the left side. Neuronal activity in the crus in response to trigeminal pain was modulated by migraine severity and the migraine phase. As the crus is strongly interconnected to higher cognitive areas in the temporal, frontal, and parietal part of the cortex our results suggest an specific cerebellar involvement in migraine. This is further supported by our finding of decreased connectivity from the crus to the thalamus and higher cortical areas in the patients. We therefore suggest an abnormally decreased inhibitory involvement of the migraine cerebellum on gating and nociceptive evaluation.