Agency and responsibility over virtual movements controlled through different paradigms of brain-computer interface
Agency is the attribution of an action to the self and is a prerequisite for experiencing responsibility over its consequences. Here we investigated agency and responsibility by studying the control of movements of an embodied avatar, via brain computer interface (BCI) technology, in immersive virtual reality. After induction of virtual body ownership by visuomotor correlations, healthy participants performed a motor task with their virtual body. We compared the passive observation of the subject's 'own' virtual arm performing the task with (1) the control of the movement through activation of sensorimotor areas (motor imagery) and (2) the control of the movement through activation of visual areas (steady-state visually evoked potentials). The latter two conditions were carried out using a brain-computer interface (BCI) and both shared the intention and the resulting action. We found that BCI-control of movements engenders the sense of agency, which is strongest for sensorimotor areas activation. Furthermore, increased activity of sensorimotor areas, as measured using EEG, correlates with levels of agency and responsibility. We discuss the implications of these results for the neural basis of agency.
The article is dedicated to neural basis of verb processing. Three verb groups were analysed: abstract, tool action and hand action verbs. We found that imageability of a verb might influence both the time of its processing and the amount of cerebral activation it is related to.
Increasing evidence suggests that neuronal communication is a defining property of functionally specialized brain networks and that it is implemented through synchronization between population activities of distinct brain areas. The detection of long-range coupling in electroencephalography (EEG) and magnetoencephalography (MEG) data using conventional metrics (such as coherence or phase-locking value) is by definition contaminated by spatial leakage. Methods such as imaginary coherence, phase-lag index or orthogonalized amplitude correlations tackle spatial leakage by ignoring zero-phase interactions. Although useful, these metrics will by construction lead to false negatives in cases where true zero-phase coupling exists in the data and will underestimate interactions with phase lags in the vicinity of zero. Yet, empirically observed neuronal synchrony in invasive recordings indicates that it is not uncommon to find zero or close-to-zero phase lag between the activity profiles of coupled neuronal assemblies. Here, we introduce a novel method that allows us to mitigate the undesired spatial leakage effects and detect zero and near zero phase interactions. To this end, we propose a projection operation that operates on sensor-space cross-spectrum and suppresses the spatial leakage contribution but retains the true zero-phase interaction component. We then solve the network estimation task as a source estimation problem defined in the product space of interacting source topographies. We show how this framework provides reliable interaction detection for all phase-lag values and we thus refer to the method as Phase Shift Invariant Imaging of Coherent Sources (PSIICOS). Realistic simulations demonstrate that PSIICOS has better detector characteristics than existing interaction metrics. Finally, we illustrate the performance of PSIICOS by applying it to real MEG dataset recorded during a standard mental rotation task. Taken together, using analytical derivations, data simulations and real brain data, this study presents a novel source-space MEG/EEG connectivity method that overcomes previous limitations and for the first time allows for the estimation of true zero-phase coupling via non-invasive electrophysiological recordings.
In this research we compare the performance of different data mining techniques in the analysis of electroencephalogram (EEG) data. We study the question od predicting post-comatose neuro-developmental scores based mainly on statistical features of the EEG recordings. We compare results from applying different data mining techniques, such as the Elastic Net, Lasso, Gaussian Support Vector Regression and Random Forest Regression. We also compare the results produced with different matrix completion methods.
Manifestations of attentional lapses in auditory evoked potential
The Abstract book contains the abstracts of the posters presentations of the participants of the Methodological school: Methods of data processing in EEg and MEG, Moscow, 16-30th of April, 2013. The School was devoted to the theoretical and practical aspects of the contemporary methods of the dynamic mapping of brain activity by analysis of multichannel MEG and EEG.
The Abstract book contains the abstracts of the posters presentations of the participants of the Methodological school: Methods of data processing in EEG and MEG, Moscow, 16-30th of April, 2013. The School was devoted to the theoretical and practical aspects of the contemporary methods of the dynamic mapping of brain activity by analysis of multichannel MEG and EEG.
The distractive effects on attentional task performance in different paradigms are analyzed in this paper. I demonstrate how distractors may negatively affect (interference effect), positively (redundancy effect) or neutrally (null effect). Distractor effects described in literature are classified in accordance with their hypothetical source. The general rule of the theory is also introduced. It contains the formal prediction of the particular distractor effect, based on entropy and redundancy measures from the mathematical theory of communication (Shannon, 1948). Single- vs dual-process frameworks are considered for hypothetical mechanisms which underpin the distractor effects. Distractor profiles (DPs) are also introduced for the formalization and simple visualization of experimental data concerning the distractor effects. Typical shapes of DPs and their interpretations are discussed with examples from three frequently cited experiments. Finally, the paper introduces hierarchical hypothesis that states the level-fashion modulating interrelations between distractor effects of different classes.
This article describes the expierence of studying factors influencing the social well-being of educational migrants as mesured by means of a psychological well-being scale (A. Perrudet-Badoux, G.A. Mendelsohn, J.Chiche, 1988) previously adapted for Russian by M.V. Sokolova. A statistical analysis of the scale's reliability is performed. Trends in dynamics of subjective well-being are indentified on the basis the correlations analysis between the condbtbions of adaptation and its success rate, and potential mechanisms for developing subjective well-being among student migrants living in student hostels are described. Particular attention is paid to commuting as a factor of adaptation.