A brief review of the EEG literature on mindfulness and fear extinction and its potential implications for posttraumatic stress symptoms (PTSS)

Neuroimaging studies in the area of mindfulness research have provided preliminary support for the idea of fear extinction as a plausible underlying mechanism through which mindfulness exerts its positive benefits. Whilst brain regions identified in the fear extinction network are typically found at a subcortical level, studies have also demonstrated the feasibility of cortical measures of the brain, such as electroencephalogram (EEG), in implying subcortical activations of the fear extinction network. Such EEG studies have also found evidence of a relationship between brain reactivity to unpleasant stimuli (i.e., fear extinction) and severity of posttraumatic stress symptoms (PTSS). Therefore, the present paper seeks to briefly review the parallel findings between the neurophysiological literature of mindfulness and fear extinction (particularly that yielded by EEG measures), and discusses the implications of this for fear-based psychopathologies, such as trauma, and finally presents suggestions for future studies. This paper also discusses the clinical value in integrating EEG in psychological treatment for trauma, as it holds the unique potential to detect neuromarkers, which may enable earlier diagnoses, and can also provide neurofeedback over the course of treatment

Enhancement and optimization of a multi-command-based brain-computer interface

Brain-computer interfaces (BCI) assist disabled person to control many appliances without any physically interaction (e.g., pressing a button). SSVEP is brain activities elicited by evoked signals that are observed by visual stimuli paradigm. In this dissertation were addressed the problems which are oblige more usability of BCI-system by optimizing and enhancing the performance using particular design. Main contribution of this work is improving brain reaction response depending on focal approaches

Psychopathic Traits and P3 Modulation During Simple and Complex Target Detection Tasks

Psychopathy is notable for traits of impulsivity, irresponsibility, and proneness to boredom, characteristics that are all substrates of executive function. However, event-related potential (ERP) P3 studies of attention-related abnormalities in the context of psychopathic traits have yielded inconsistent results (Gao & Raine, 2009). The current study attempted to address these discrepancies by investigating the effects of psychopathic traits on P3s during two attentional tasks. Two groups of ERP participants (n = 28) who had high (T score greater than or equal to 50) or low (T score less than or equal to 40) Psychopathic Personality Inventory – Revised (PPI-R; Lilienfeld & Widows, 2005) total scores were recruited from a larger sample (n = 181) of undergraduate students. ERP participants completed a standard oddball (SDO) task and a continuous performance task (CPT) during which they responded to target stimuli while their EEG was recorded. Contrary to my hypotheses, individuals with high PPI-R total scores performed significantly less accurately on both tasks compared to those with low PPI-R total scores, yet, total PPI-R scores were not related to P3 amplitude. High TriPM Disinhibition scores were associated with decreased P3 amplitudes during the complex CPT task, but not the simple SDO task. My results suggest that impulsive-externalizing traits that are often a hallmark of psychopathy, are associated with an attentional deficiency

Efficiency evaluation of external environments control using bio-signals

There are many types of bio-signals with various control application prospects. This dissertation regards possible application domain of electroencephalographic signal. The implementation of EEG signals, as a source of information used for control of external devices, became recently a growing concern in the scientific world. Application of electroencephalographic signals in Brain-Computer Interfaces (BCI) (variant of Human-Computer Interfaces (HCI)) as an implement, which enables direct and fast communication between the human brain and an external device, has become recently very popular. Currently available on the market, BCI solutions require complex signal processing methodology, which results in the need of an expensive equipment with high computing power. In this work, a study on using various types of EEG equipment in order to apply the most appropriate one was conducted. The analysis of EEG signals is very complex due to the presence of various internal and external artifacts. The signals are also sensitive to disturbances and non-stochastic, what makes the analysis a complicated task. The research was performed on customised (built by the author of this dissertation) equipment, on professional medical device and on Emotiv EPOC headset. This work concentrated on application of an inexpensive, easy to use, Emotiv EPOC headset as a tool for gaining EEG signals. The project also involved application of embedded system platform - TS-7260. That solution caused limits in choosing an appropriate signal processing method, as embedded platforms characterise with a little efficiency and low computing power. That aspect was the most challenging part of the whole work. Implementation of the embedded platform enables to extend the possible future application of the proposed BCI. It also gives more flexibility, as the platform is able to simulate various environments. The study did not involve the use of traditional statistical or complex signal processing methods. The novelty of the solution relied on implementation of the basic mathematical operations. The efficiency of this method was also presented in this dissertation. Another important aspect of the conducted study is that the research was carried out not only in a laboratory, but also in an environment reflecting real-life conditions. The results proved efficiency and suitability of the implementation of the proposed solution in real-life environments. The further study will focus on improvement of the signal-processing method and application of other bio-signals - in order to extend the possible applicability and ameliorate its effectiveness

Brain Signatures of Embodied Semantics and Language: A Consensus Paper

According to embodied theories (including embodied, embedded, extended, enacted, situated, and grounded approaches to cognition), language representation is intrinsically linked to our interactions with the world around us, which is reflected in specific brain signatures during language processing and learning. Moving on from the original rivalry of embodied vs. amodal theories, this consensus paper addresses a series of carefully selected questions that aim at determining when and how rather than whether motor and perceptual processes are involved in language processes. We cover a wide range of research areas, from the neurophysiological signatures of embodied semantics, e.g., event-related potentials and fields as well as neural oscillations, to semantic processing and semantic priming effects on concrete and abstract words, to first and second language learning and, finally, the use of virtual reality for examining embodied semantics. Our common aim is to better understand the role of motor and perceptual processes in language representation as indexed by language comprehension and learning. We come to the consensus that, based on seminal research conducted in the field, future directions now call for enhancing the external validity of findings by acknowledging the multimodality, multidimensionality, flexibility and idiosyncrasy of embodied and situated language and semantic processes

Strategic control processes in episodic memory and beyond

The evaluation of past experience is influenced both by the strength of retrieved memories and factors in the immediate retrieval environment, including emphasised goals and cued expectations. However, the laboratory study of episodic memory has neglected such environmental influences, despite their overt contribution to real-world decision outcomes. The aim of this PhD thesis was to rectify this neglect, and clarify the interaction of memory evidence and environmental strategies in the service of strategic memory control. A related aim was to investigate whether control processes identified in the isolated domain of episodic memory in fact performed a more general or “cross-domain” function. An initial series of behavioural experiments (Experiments 1-3) elucidated an overlooked source of strategic bias in the standard recognition environment – implicit goal emphasis imparted by question format. Experiment 4 investigated whether the question bias was commonly enacted across different domains of evaluation, yielding modest evidence in favour of this underlying cross-domain function. Experiment 5 instantiated more explicit manipulation of goal emphasis and cued expectation, and recovered independent and opposing strategic effects of these two environmental factors, emerging across episodic and non-episodic domains. Experiment 6 employed a simultaneous EEG-fMRI approach to elucidate the neural correlates of memory control, identifying a modulation of the late positive event-related potential during the resolution of mnemonic conflict, which was sourced to BOLD variation in regions of the rostral cingulate zone and intraparietal sulcus. Experiment 7 used pupillometry to examine pupil-linked autonomic systems that have also been implicated in memory control, and isolated two distinct components of the dilation response evoked during environmental conflict – an “early amplitude” unexpected familiarity effect and a “trailing slope” uncertainty effect. The findings illuminate the cross-domain underpinnings of an adaptive memory control system, evidenced in behaviour and across different functional neuroimaging modalities, and across episodic and non-episodic domains of evaluation

BCIs and mobile robots for neurological rehabilitation: practical applications of remote control. Remote control of mobile robots applied in non-invasive BCI for disabled users afflicted by motor neurons diseases

This project aims at testing the possible advantages of introducing a mobile robot as a physical input/output device in a Brain Computer Interface (BCI) system. In the proposed system, the actions triggered by the subject’s brain activity results in the motions of a physical device in the real world, and not only in a modification of a graphical interface. A goal-based system for destination detecting and the high entertainment level offered by controlling a mobile robot are hence main features for actually increase patients' life quality leve

Brain Computer Interfaces: OpenViBE as a Platform for a P300 Speller

Aside from hardware, a major component of a Brain Computer Interface is the software that provides the tools for translating raw acquired brain signals into commands to control an application or a device. There’s a range of software, some proprietary, like MATLAB and some free and open source (FOSS), accessible under the GNU General Public License (GNU GPL). OpenViBE is one such freely accessible software. This thesis carries out a functionality and usability test of the platform, looking at its portability, architecture and communication protocols. To investigate the feasibility of reproducing the P300 xDAWN speller BCI presented by OpenViBE, users focused on a character on a 6x6 alphanumeric grid which contained a sequence of random flashes of the rows and columns. Visual stimulus is presented to a user every time the character they are focusing on is highlighted in a row or column. A TMSi analog-to-digital converter was used together with a 32-channel active electrode cap (actiCAP) to record user’s Electroencephalogram (EEG) which was then used in an offline session to train the spatial filter algorithm, and the classifier to identify the P300 evoked potentials, elicited as a user’s reaction to an external stimulus. In an online session, the users tried to spell with the application using the power of their brain signal. Aspects of evoked potentials (EP), both auditory (AEP) and visual (VEP) are further investigated as a validation of results of the P300 speller

Characterization of Neuroimage Coupling Between EEG and FMRI Using Within-Subject Joint Independent Component Analysis

The purpose of this dissertation was to apply joint independent component analysis (jICA) to electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to characterize the neuroimage coupling between the two modalities. EEG and fMRI are complimentary imaging techniques which have been used in conjunction to investigate neural activity. Understanding how these two imaging modalities relate to each other not only enables better multimodal analysis, but also has clinical implications as well. In particular, Alzheimer’s, Parkinson’s, hypertension, and ischemic stroke are all known to impact the cerebral blood flow, and by extension alter the relationship between EEG and fMRI. By characterizing the relationship between EEG and fMRI within healthy subjects, it allows for comparison with a diseased population, and may offer ways to detect some of these conditions earlier. The correspondence between fMRI and EEG was first examined, and a methodological approach which was capable of informing to what degree the fMRI and EEG sources corresponded to each other was developed. Once it was certain that the EEG activity observed corresponded to the fMRI activity collected a methodological approach was developed to characterize the coupling between fMRI and EEG. Finally, this dissertation addresses the question of whether the use of jICA to perform this analysis increases the sensitivity to subcortical sources to determine to what degree subcortical sources should be taken into consideration for future studies. This dissertation was the first to propose a way to characterize the relationship between fMRI and EEG signals using blind source separation. Additionally, it was the first to show that jICA significantly improves the detection of subcortical activity, particularly in the case when both physiological noise and a cortical source are present. This new knowledge can be used to design studies to investigate subcortical signals, as well as to begin characterizing the relationship between fMRI and EEG across various task conditions