EEG-based cognitive control behaviour assessment: an ecological study with professional air traffic controllers

Several models defining different types of cognitive human behaviour are available. For this work, we have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools able to assess at which level of cognitive control the operator is dealing with the considered task, that is if he/she is performing the task as an automated routine (skill level), as procedures-based activity (rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such behaviours have been evaluated from a neurophysiological point of view, for example, by considering brain activity variations across the different SRK levels. Therefore, the proposed study aimed to investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers, demonstrated that specific brain features could characterize and discriminate the different SRK levels, therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic setting

Self-Referential Processing: An Investigation of the Mediating Role of Alpha Power

The EEG correlates of valenced self- and other-referential processing (SRP-ORP) are relatively little understood. This study examined the immediate effects of mindfulness meditation (MM) and EEG alpha neurofeedback (NFB) on resting state EEG alpha amplitudes and alpha event related (de-)synchronization (ERD/S) during an experimental implicit and explicit SRP-ORP task. Undergraduate students (n = 93) were randomized to a single session of MM, NFB alpha synchronization training (“alpha-up”), NFB alpha desynchronization training (“alpha-down”), or sham (placebo control) NFB before completing the Visual-Verbal Self-Other Referential Processing Task (VV-SORP-T). A reduction in resting-state alpha power over posterior cortex was observed across groups relative to pre-treatment baseline, with no differential effects observed between groups. During both SRP and ORP, however, less negative affect (NA) was experienced by participants in the alpha-down group. Alpha ERD was highest during negative ORP relative to other task conditions across groups, with the alpha-down group trending toward showing increased ERD across all conditions of the VV-SORP-T relative to the alpha-up group. Study limitations and future research directions are discussed

Why do infants imitate selectively? Neural correlates of infants’ action understanding in the head-touch paradigm

Imitation is an important social learning mechanism for young infants exploring the world. Interestingly, infants do not imitate every action they observe – they rather do so selectively. Fourteen-month-olds predominantly imitated an unusual and inefficient action (turning on a lamp with one’s forehead) when the model’s hands were free compared to when the model’s hands were occupied (Gergely et al., 2002). Behavioral scientists have proposed contrasting explanatory accounts, differing with regard to the assumed level of infants’ cognitive abilities. Rational-imitation accounts suggest that infants selectively imitate unusual actions because they are surprised by the inefficiency of the action (Gergely & Csibra, 2003). In contrast, non-rational imitation accounts propose that selective imitation depends on more basic factors such as motor abilities (Paulus et al., 2011a,b). The integrative model by Zmyj and Buttelmann (2014) represents the first attempt to put together these opposing theories. Both accounts may operate on different processing levels. Bottom-up processes are related to non-rational imitation accounts, whereas top-down processing is based on the assumptions of the rational-imitation accounts. Despite the large body of behavioral research on selective imitation, the question of what are the neural mechanisms underlying these processes remains unanswered. In my dissertation, I aimed to uncover the underlying cognitive processes during the observation of head-touch actions by recording infants’ neurophysiological responses in three empirical studies. To test the assumptions of the top-down processes linked to the rational-imitation accounts, I examined neural markers associated with violation of expectation (VOE) in an adaptation of the head-touch paradigm. Overall, results suggest that 12- to 14-month-old infants, but not 9-month-old infants, display VOE when observing a person performing an inefficient head touch. This VOE response is context-dependent and is elicited when the model’s hands are free but not when the hands are restrained. In Study 1, VOE has been linked to a reduction in mu power in response to the unexpected head touch. In Study 2, this finding was extended such that when 12- to 14-month-old infants observed an unexpected head touch, their brains responded with increased attentional engagement (enhanced Nc amplitude) and a detection of a semantic violation (N400 component). Finally, in Study 3, in the absence of contextual information, 1-year-olds discriminated between hand- and head-touch outcomes on the Nc component only. Thus, infants require information of the action context to detect semantic violations within the head-touch paradigm. To conclude, the studies presented in my dissertation have paved the way to further our understanding of infants’ action perception and observational learning. Understanding the neural mechanisms of infants’ action perception in more depth, will help us to adequately foster the ideal observational learning conditions of novel actions. The results of this dissertation suggest that presenting infants with surprising action means puts them in an optimal receptive state for knowledge acquisition

Magnetoencephalography in Stroke Recovery and Rehabilitation

Magnetoencephalography (MEG) is a non-invasive neurophysiological technique used to study the cerebral cortex. Currently, MEG is mainly used clinically to localize epileptic foci and eloquent brain areas in order to avoid damage during neurosurgery. MEG might, however, also be of help in monitoring stroke recovery and rehabilitation. This review focuses on experimental use of MEG in neurorehabilitation. MEG has been employed to detect early modifications in neuroplasticity and connectivity, but there is insufficient evidence as to whether these methods are sensitive enough to be used as a clinical diagnostic test. MEG has also been exploited to derive the relationship between brain activity and movement kinematics for a motor-based brain-computer interface. In the current body of experimental research, MEG appears to be a powerful tool in neurorehabilitation, but it is necessary to produce new data to confirm its clinical utility

Event related (de-)synchronization patterns in actual and imagined hand movements

Projecte final de carrera realitzat en col.laboració amb Philips ResearchThis project presents different signal processing techniques, such as Principal Component Analysis (PCA) and Common Spatial Patterns (CSP), applied to characterize the reactivity of central rhythms in the alpha and beta bands during self paced voluntary and imaginary movement. The idea is to allow people to control devices, or interact with machines by simply thinking. To do so, we monitor the brain activity using electroencephalogram (EEG) measurements which record the signals from electrodes positioned on the scalp. The objective is to use motor imagery signals to build a brain computer interface, able to learn from data analyzed before, using the properties of neural networks. The possibility of designing an intuitive communication system between a brain and a computer, available to be operated by everyone, even by people with severe motor impairments, is the main objective of this stud

A P300-speller based on event-related spectral perturbation (ERSP)

A brain-computer interface (BCI) P300 speller is a novel technique that helps people spell words using the electroencephalography (EEG) without the involvement of muscle activities. However, only time domain ERP features (P300) are used for controlling of the BCI speller. In this paper, we investigated the time-frequency EEG features for the P300-based brain-computer interface speller. A signal preprocessing method integrated ensemble average, principal component analysis, and independent component analysis to remove noise and artifacts in the EEG data. A time-frequency analysis based on wavelet transform was carried out to extract event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) features. Results showed that the proposed signal processing method can effectively extract EEG time-frequency features in the P300 speller, suggesting that ERSP and ITC may be useful for improving the performance of BCI P300 speller. © 2012 IEEE.published_or_final_versio

Analysis of the structure of time-frequency information in electromagnetic brain signals

This thesis encompasses methodological developments and experimental work aimed at revealing information contained in time, frequency, and time–frequency representations of electromagnetic, specifically magnetoencephalographic, brain signals. The work can be divided into six endeavors. First, it was shown that sound slopes increasing in intensity from undetectable to audible elicit event-related responses (ERRs) that predict behavioral sound detection. This provides an opportunity to use non-invasive brain measures in hearing assessment. Second, the actively debated generation mechanism of ERRs was examined using novel analysis techniques, which showed that auditory stimulation did not result in phase reorganization of ongoing neural oscillations, and that processes additive to the oscillations accounted for the generation of ERRs. Third, the prerequisites for the use of continuous wavelet transform in the interrogation of event-related brain processes were established. Subsequently, it was found that auditory stimulation resulted in an intermittent dampening of ongoing oscillations. Fourth, information on the time–frequency structure of ERRs was used to reveal that, depending on measurement condition, amplitude differences in averaged ERRs were due to changes in temporal alignment or in amplitudes of the single-trial ERRs. Fifth, a method that exploits mutual information of spectral estimates obtained with several window lengths was introduced. It allows the removal of frequency-dependent noise slopes and the accentuation of spectral peaks. Finally, a two-dimensional statistical data representation was developed, wherein all frequency components of a signal are made directly comparable according to spectral distribution of their envelope modulations by using the fractal property of the wavelet transform. This representation reveals noise buried processes and describes their envelope behavior. These examinations provide for two general conjectures. The stability of structures, or the level of stationarity, in a signal determines the appropriate analysis method and can be used as a measure to reveal processes that may not be observable with other available analysis approaches. The results also indicate that transient neural activity, reflected in ERRs, is a viable means of representing information in the human brain.reviewe