Cognitive Workload Assessment Based on the Tensorial Treatment of EEG Estimates of Cross-Frequency Phase Interactions

Abstract

The decoding of conscious experience, based on non-invasive measurements, has become feasible by tailoring machine learning techniques to analyse neuroimaging data. Recently, functional connectivity graphs (FCGs) have entered into the picture. In the related decoding scheme, FCGs are treated as unstructured data and, hence, their inherent format is overlooked. To alleviate this, tensor subspace analysis (TSA) is incorporated for the parsimonious representation of connectivity data. In addition to the particular methodological innovation, this work also makes a contribution at a conceptual level by encoding in FCGs cross-frequency coupling apart from the conventional frequency-specific interactions. Working memory related tasks, supported by networks oscillating at different frequencies, are good candidates for assessing the novel approach. We employed surface EEG recordings when the subjects were repeatedly performing a mental arithmetic task of five cognitive workload levels. For each trial, an FCG was constructed based on phase interactions within and between Frontal (θ) and Parieto-Occipital (α2) neural activities, which are considered to reflect the function of two distinct working memory subsystems. Based on the TSA representation, a remarkably high correct-recognition-rate (96%) of the task difficulties was achieved using a standard classifier. The overall scheme is computational efficient and therefore potentially useful for real-time and personalized applications.

Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes

“…Additionally, the Dynome project should pay attention to the ''electrophysiological and anatomical signature'' of a cognitive function (e.g. working memory is located over frontal brain areas and is expressed mainly within h frequency; Dimitriadis et al 2014) and also to the understanding of brain dynamics in both spatial and temporal scales through dynomics (dynamic ? connectomics) which will drive new understanding of brain function and dysfunction in cognition.…”

Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury

“…Our approach further validates the TSA representation of brain graphs as a valuable algorithmic tool for building reliable connectomic biomarkers in brain disorders/diseases like in mTBI. In previous studies, we employed TSA for the classification of EEG cognitive workload levels (Dimitriadis et al, 2013b(Dimitriadis et al, ,2015c and also for analyzing intra-frequency FCGs on mTBI at resting-state MEG (Dimitriadis et al, 2015b).…”

“…Intrinsic coupling modes (ICMs) in ongoing activity are thought to reflect the action of two different coupling mechanisms (Engel et al, 2001): one that arises from phase coupling of band-limited oscillatory signals, and another one that results from coupled aperiodic fluctuations of signal envelopes. When studying ICMs, apart from exploring the relationship between same frequency signals, it is highly interesting to also quantify functional relationships between signals of different frequencies (Jensen and Colgin, 2007;Palva and Palva, 2011;Jirsa and Muller, 2013;Dimitriadis et al, 2015cDimitriadis et al, ,d,2016, as this cross-frequency coupling (CFC) has been hypothesized to represent the mechanism of interaction between local and global processes and therefore it is directly related to the integration of distributed information.…”

Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs)

“…The resulting signals were submitted to independent component analysis (ICA) using the extended Infomax algorithm as implemented in EEGLAB (Delorme and Makeig, 2004). A given independent component was considered to reflect ocular or cardiac artifacts if more than 30% of its z-score kurtosis or skewness values, respectively, were outside ±2 of the distribution mean (Escudero et al, 2011; Antonakakis et al, 2013; Dimitriadis et al, 2014). The remaining ICs were used to reconstruct a relatively artifact-free signal.…”

“…Many researchers world-wide presented connectomic biomarkers for various brain disorders/diseases and for that reason, their reproducibility should be explored (Kaiser, 2013; Dimitriadis et al, 2015b,c,d, 2016a,b). The choice of the appropriate preprocessing steps to achieve reproducible measurements is more than significant.…”