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While it is well-established that instantaneous changes in neuronal networks’ states lead to variability in brain responses and behavior, the mechanisms causing this variability are poorly understood. Insights into the organization of underlying system dynamics may be gained by examining the temporal structure of network state fluctuations, such as reflected in instantaneous cortical excitability. Using the early part of single-trial somatosensory evoked potentials in the human EEG, we non-invasively tracked the magnitude of excitatory post-synaptic potentials in the primary somatosensory cortex (BA 3b) in response to median nerve stimulation. Fluctuations in cortical excitability demonstrated long-range temporal dependencies decaying according to a power-law across trials. As these dynamics covaried with pre-stimulus alpha oscillations, we establish a functional link between ongoing and evoked activity and argue that the co-emergence of similar temporal power-laws may originate from neuronal networks poised close to a critical state, representing a parsimonious organizing principle of neural variability.

Multimodal data are ubiquitous in engineering, communications, robotics, computer vision, or more generally speaking in industry and the sciences. All disciplines have developed their respective sets of analytic tools to fuse the information that is available in all measured modalities. In this paper, we provide a review of classical as well as recent machine learning methods (specifically factor models) for fusing information from functional neuroimaging techniques such as: LFP, EEG, MEG, fNIRS, and fMRI. Early and late fusion scenarios are distinguished, and appropriate factor models for the respective scenarios are presented along with example applications from selected multimodal neuroimaging studies. Further emphasis is given to the interpretability of the resulting model parameters, in particular by highlighting how factor models relate to physical models needed for source localization. The methods we discuss allow for the extraction of information from neural data, which ultimately contributes to 1) better neuroscientific understanding; 2) enhance diagnostic performance; and 3) discover neural signals of interest that correlate maximally with a given cognitive paradigm. While we clearly study the multimodal functional neuroimaging challenge, the discussed machine learning techniques have a wide applicability, i.e., in general data fusion, and may thus be informative to the general interested reader.

Brain-computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies.

Do ongoing brain states determine conscious perception of an upcoming stimulus? Using the high temporal resolution of EEG, we investigated the relationship between prestimulus neuronal oscillations and the perceptibility of two competing somatosensory stimuli embedded in a backward masking paradigm. We identified two prestimulus EEG signatures predictive for a suprathreshold yet weak target stimulus to become perceptually resistant against masking by a stronger distractor stimulus: (i) over left frontal cortex a desynchronization of the regional beta rhythm (20 Hz) 500 msec prior to a perceived target, and (ii) a subsequent additional attenuation of both mu (10 Hz) and beta "idling" rhythms over those pericentral sensorimotor corticeswhich are going to process the upcoming target stimulus. Furthermore, across subjects the probability for target perception strongly correlates with the individual absolute level of pretarget amplitudes in these frequency bands and location s. These signatures significantly differed from the EEG characteristics preceding detected and undetected single stimuli. We suggest that the early activation of left frontal areas involved in top-down attentional control is critical for preventing backward masking and leads the preparation of primary sensory cortices: The ensuing prestimulus suppression of sensory idling rhythms warrants an intensified poststimulus processing, and thus, effectively promotes conscious perception of suprathreshold target stimuli embedded into an ecologically relevant condition featuring competing environmental stimuli.

We introduce Focal Vector Field Reconstruction (FVR), a novel technique for the inverse imaging of vector fields. The method was designed to simultaneously achieve two goals: a) invariance with respect to the orientation of the coordinate system, and b) a preference for sparsity of the solutions and their spatial derivatives. This was achieved by defining the regulating penalty function, which renders the solutions unique, as a global l(1)-norm of local l(2)-norms. We show that the method can be successfully used for solving life EEG inverse problem. In the joint localization of 2-3 simulated dipoles, FVR always reliably recovers the true sources. The competing methods have limitations in distinguishing close sources because their estimates are either too smooth (LORETA, Minimum l(1)-norm) or too scattered (Minimum l(2)-norm). In both noiseless and noisy simulations, FVR has the smallest localization error according to the Earth Mover's Distance (EMD), which is introduced here as a meaningful measure to compare arbitrary source distributions. We also apply the method to the simultaneous localization of left and right somatosensory N20 generators front real EEG recordings. Compared to its peers FVR was the only method that delivered correct location of the source in the somatosensory area of each hemisphere in accordance with neurophysiological prior knowledge.