Functional Source Separation for EEG-fMRI Fusion: Application to Steady-State Visual Evoked Potentials

Abstract

Neurorobotics is one of the most ambitious fields in robotics, driving integration of interdisciplinary data and knowledge. One of the most productive areas of interdisciplinary research in this area has been the implementation of biologically-inspired mechanisms in the development of autonomous systems. Specifically, enabling such systems to display adaptive behavior such as learning from good and bad outcomes, has been achieved by quantifying and understanding the neural mechanisms of the brain networks mediating adaptive behaviors in humans and animals. For example, associative learning from aversive or dangerous outcomes is crucial for an autonomous system, to avoid dangerous situations in the future. A body of neuroscience research has suggested that the neurocomputations in the human brain during associative learning involve re-shaping of sensory responses. The nature of these adaptive changes in sensory processing during learning however are not yet well enough understood to be readily implemented into on-board algorithms for robotics application. Toward this overall goal, we record the simultaneous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), characterizing one candidate mechanism, i.e., large-scale brain oscillations. The present report examines the use of Functional Source Separation (FSS) as an optimization step in EEG-fMRI fusion that harnesses timing information to constrain the solutions that satisfy physiological assumptions. We applied this approach to the voxel-wise correlation of steady-state visual evoked potential (ssVEP) amplitude and blood oxygen level-dependent imaging (BOLD), across both time series. The results showed the benefit of FSS for the extraction of robust ssVEP signals during simultaneous EEG-fMRI recordings. Applied to data from a 3-phase aversive conditioning paradigm, the correlation maps across the three phases (habituation, acquisition, extinction) show converging results, notably major overlapping areas in both primary and extended visual cortical regions, including calcarine sulcus, lingual cortex, and cuneus. In addition, during the acquisition phase when aversive learning occurs, we observed additional correlations between ssVEP and BOLD in the anterior cingulate cortex (ACC) as well as the precuneus and superior temporal gyrus.

Visual experience modulates whole‐brain connectivity dynamics: A resting‐state fMRI study using the model of radiologists

“…Compared with the NC group, the IR group has higher coherence within FCs responsible for visual processing. Specifically, significant higher FCs were observed between the left LING (belongs to visual area V2) and the sub‐regions of the primary visual cortex (visual area V1) including the bilateral SOG (BA17/18), left CAL, and left CUN (collectively referred to as V1 [Ji et al, 2019] in Figure 4). Visual expertise is an acquired skill through learning hundreds of cases with domain‐specific visual features (Annis & Palmeri, 2019).…”

“…The acquisition of expert knowledge may be accompanied by an increase in the cohesion of visual processing functions. In the process of visual learning, the neural computation in the human brain remodels the sensory responses (Ji et al, 2019), which makes the brain more perceptive and makes it easier for subjects to recognize specific features in images (Haller & Radue, 2005). The ventral visual stream is thought to be a basis of visual recognition (Ungerleider & Haxby, 1994), which starts with V1, relays to V2, and finally reaches the inferior temporal cortex (Song et al, 2017).…”

“…In this study, a significant higher FC was observed in the IR group between regions belonging to V2 and V1 within the ventral visual stream. LING belonging to visual area V2 is a brain region for early visual processing that has been found to be associated with the encoding of image features (Machielsen, Rombouts, Barkhof, Scheltens, & Witter, 2000) and skill learning such as word learning (Marcotte & Ansaldo, 2014), associative visual learning (Ji et al, 2019), perceptual learning (Liu et al, 2016), and always to be activated among visual experts according to the evidence from meta‐analysis (Neumann, Lotze, & Eickhoff, 2016). In addition, LING has been observed to be co‐activated across the habituation and acquisition phases of associative visual learning with CAL and CUN belonging to visual area V1 (Ji et al, 2019), and among presbyopia subjects with improved near‐reading abilities after perceptual learning with CUN belonging to V1 (Liu et al, 2016).…”

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

“…BSS is a popular way to enhance SSVEP responses if the frequency range of some artifacts (i.e., EMG) and the EEG overlap to a high degree. The BSS represents a group of methods that recover underlying useful signals and reject harmful artifacts through exploring the statistical independent criteria [41], [42]. For example, independent component analysis (ICA) returns independent sources which form the measured EEG signal under the assumption that they are linearly mixed [41].…”

“…The BSS represents a group of methods that recover underlying useful signals and reject harmful artifacts through exploring the statistical independent criteria [41], [42]. For example, independent component analysis (ICA) returns independent sources which form the measured EEG signal under the assumption that they are linearly mixed [41]. Therefore, it is flexible to reconstruct the EEG signal with non-artifact components to improve the quality of signals, thus enhance the stimulation frequency identification accuracy [43].…”