A new paradigm for BCI research

A new control paradigm for Brain Computer Interfaces (BCIs) is proposed. BCIs provide a means of communication direct from the brain to a computer that allows individuals with motor disabilities an additional channel of communication and control of their external environment. Traditional BCI control paradigms use motor imagery, frequency rhythm modification or the Event Related Potential (ERP) as a means of extracting a control signal. A new control paradigm for BCIs based on speech imagery is initially proposed. Further to this a unique system for identifying correlations between components of the EEG and target events is proposed and introduced

Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data

The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity. Functional networks are estimated from short non-overlapping time windows within each “trial,” using a sparse-MVAR (Multi-Variate Auto-Regressive) model. Functional “states” are then identified by partitioning the entire space of functional networks into a small number of groups/symbols via k-means clustering.The multi-trial sequence of symbols is then described by a Markov Model (MM). We show validity of this pipeline on realistic electrode-level simulated EEG data, by demonstrating its ability to discriminate “trials” from two experimental conditions in a range of scenarios. We then apply it to experimental data from two individuals using a Brain-Computer Interface (BCI) via a P300 oddball task. Using just the Markov Model parameters, we obtain statistically significant discrimination between target and non-target trials. The functional networks characterizing each ‘state’ were also highly similar between the two individuals. This work marks the first application of the Markov Model framework to infer time-varying networks from EEG/MEG data. Due to the pre-processing, results from the pipeline are orthogonal to those from conventional ERP averaging or a typical EEG microstate analysis. The results provide powerful proof-of-concept for a Markov model-based approach to analyzing the data, paving the way for its use to track rapid changes in interaction patterns as a task is being performed. MATLAB code for the entire pipeline has been made available.Peer reviewe

Mathematical mindsets increase student motivation: Evidence from the EEG

Mathematical mindset theory suggests learner motivation in mathematics may be increased by opening problems using a set of recommended ideas. However, very little evidence supports this theory. We explore motivation through self-reports while learners attempt problems formulated according to mindset theory and standard problems. We also explore neural correlates of motivation and felt-affect while participants attempt the problems. Notably, we do not tell participants what mindset theory is and instead simply investigate whether mindset problems affect reported motivation levels and neural correlates of motivation in learners. We find significant increases in motivation for mindset problems compared to standard problems. We also find significant differences in brain activity in prefrontal EEG asymmetry between problems. This provides some of the first evidence that mathematical mindset theory increases motivation (even when participants are not aware of mindset theory), and that this change is reflected in brain activity of learners attempting mathematical problems

Cortical excitability correlates with the event-related desynchronization during brain-computer interface control

Objective Brain-computer interfaces (BCIs) based on motor control have been suggested as tools for stroke rehabilitation. Some initial successes have been achieved with this approach, however the mechanism by which they work is not yet fully understood. One possible part of this mechanism is a, previously suggested, relationship between the strength of the event-related desynchronization (ERD), a neural correlate of motor imagination and execution, and corticospinal excitability. Additionally, a key component of BCIs used in neurorehabilitation is the provision of visual feedback to positively reinforce attempts at motor control. However, the ability of visual feedback of the ERD to modulate the activity in the motor system has not been fully explored. Approach We investigate these relationships via transcranial magnetic stimulation delivered at different moments in the ongoing ERD related to hand contraction and relaxation during BCI control of a visual feedback bar. Main results We identify a significant relationship between ERD strength and corticospinal excitability, and find that our visual feedback does not affect corticospinal excitability. Significance Our results imply that efforts to promote functional recovery in stroke by targeting increases in corticospinal excitability may be aided by accounting for the time course of the ERD

Affective Brain-Computer Interfacing and Methods for Affective State Detection

Affective brain–computer interfaces (aBCIs) provide a method for individuals to interact with a computer via their emotions and without needing to move. This chapter will provide an introduction to the concept of aBCIs and their uses in applications such as music therapy and affective computing. We will first review the concept of aBCIs before going on to provide a literature review of the current state-of-the-art research in affective state detection methods and their uses in aBCI. Finally, we will describe a case study; an affective brain–computer music interface (aBCMI) and its potential for use in music therapy. Emerging and established trends in aBCI, such as the use of prefrontal asymmetry measures of affective states, are identified. Additionally, a set of recommendations are provided for researchers seeking to work in the field of aBCI

Novel single trial movement classification based on temporal dynamics of EEG

Various complex oscillatory processes are involved in the generation of the motor command. The temporal dynamics of these processes were studied for movement detection from single trial electroencephalogram (EEG). Autocorrelation analysis was performed on the EEG signals to find robust markers of movement detection. The evolution of the autocorrelation function was characterised via the relaxation time of the autocorrelation by exponential curve fitting. It was observed that the decay constant of the exponential curve increased during movement, indicating that the autocorrelation function decays slowly during motor execution. Significant differences were observed between movement and no moment tasks. Additionally, a linear discriminant analysis (LDA) classifier was used to identify movement trials with a peak accuracy of 74%

Microstructural Correlates of Resilience against Major Depressive Disorder: Epigenetic Mechanisms?

Mental disorders are a major cause of long-term disability and are a direct cause of mortality, with approximately 800.000 individuals dying from suicide every year worldwide - a high proportion of them related to major depressive disorder (MDD)^1^. Healthy relatives of patients with major depressive disorder (MDD) are at risk to develop the disease. This higher vulnerability is associated with structural^2-4^ and functional brain changes^5^. However, we found using high angular resolution diffusion imaging (HARDI) with 61 diffusion directions that neuron tracts between frontal cortices and limbic as well as temporal and parietal brain regions are characterized by better diffusion coefficients in unaffected relatives (UHR), who managed to stay healthy, compared to healthy volunteers without any family history for a psychiatric disease (HC). Moreover, those UHR with stronger fibre connections better managed incidences of adversity in early life without later developing depression, while in HC axonal connections were found to be decreased when they had early-life adversity. Altogether these findings indicate the presence of stronger neural fibre connections in UHR, which seem to be associated with resilience against environmental stressors, which we suggest occur through epigenetic mechanisms

The relationship between shame and guilt: cultural comparisons between Ireland and the United Arab Emirates

© 2018, Informa UK Limited, trading as Taylor & Francis Group. The current study examines whether proneness to shame and guilt is related to the cultural dimensions of collectivism and individualism. Two groups of participants from Ireland (n = 120) and the United Arab Emirates (UAE) (n = 115) completed measures assessing collectivism, individualism, and shame and guilt proneness. Results indicated that both samples displayed similar levels of individualism and collectivism. The UAE sample reported significantly higher levels of guilt proneness and shame proneness characterised by negative self-evaluation. In contrast, the Irish sample displayed significantly higher levels of shame characterised by withdrawal tendencies. Guilt was positively correlated with individualism, but shame was not correlated with either scores on collectivism or individualism. Young Arab women appear to experience higher levels of guilt and shame characterised by negative self-evaluation in comparison to their Irish counterparts who displayed higher levels of guilt proneness

Inkjet printing for pharmaceutics - A review of research and manufacturing.

Global regulatory, manufacturing and consumer trends are driving a need for change in current pharmaceutical sector business models, with a specific focus on the inherently expensive research costs, high-risk capital-intensive scale-up and the traditional centralised batch manufacturing paradigm. New technologies, such as inkjet printing, are being explored to radically transform pharmaceutical production processing and the end-to-end supply chain. This review provides a brief summary of inkjet printing technologies and their current applications in manufacturing before examining the business context driving the exploration of inkjet printing in the pharmaceutical sector. We then examine the trends reported in the literature for pharmaceutical printing, followed by the scientific considerations and challenges facing the adoption of this technology. We demonstrate that research activities are highly diverse, targeting a broad range of pharmaceutical types and printing systems. To mitigate this complexity we show that by categorising findings in terms of targeted business models and Active Pharmaceutical Ingredient (API) chemistry we have a more coherent approach to comparing research findings and can drive efficient translation of a chosen drug to inkjet manufacturing.This project was supported by (i) the UK Engineering and Physical Sciences Research Council and industrial partners in the Programme Grant number EP/H018913/1 ‘Innovation in Industrial Inkjet Technology’, (ii) EPSRC Centre for Innovative Manufacturing in Continuous Manufacturing and Crystallisation (EP/1033459/1) and (iii) Department of Business, Innovation and Skill’s (BIS) Advanced Manufacturing Supply Chain Initiative (AMSCI) funded Project ‘Remedies’ (TS/L006529/1).This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.ijpharm.2015.03.01