Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback

Optimizing neurofeedback (NF) and brain–computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user’s control ability from neurophysiological or psychological measures. In comparison, how context factors influence NF/BCI performance is largely unexplored. We here investigate whether a competitive multi-user condition leads to better NF/BCI performance than a single-user condition. We implemented a foot motor imagery (MI) NF with mobile electroencephalography (EEG). Twenty-five healthy, young participants steered a humanoid robot in a single-user condition and in a competitive multi-user race condition using a second humanoid robot and a pseudo competitor. NF was based on 8–30 Hz relative event-related desynchronization (ERD) over sensorimotor areas. There was no significant difference between the ERD during the competitive multi-user condition and the single-user condition but considerable inter-individual differences regarding which condition yielded a stronger ERD. Notably, the stronger condition could be predicted from the participants’ MI-induced ERD obtained before the NF blocks. Our findings may contribute to enhance the performance of NF/BCI implementations and highlight the necessity of individualizing context factor

A Riemannian Modification of Artifact Subspace Reconstruction for EEG Artifact Handling

Artifact Subspace Reconstruction (ASR) is an adaptive method for the online or offline correction of artifacts comprising multichannel electroencephalography (EEG) recordings. It repeatedly computes a principal component analysis (PCA) on covariance matrices to detect artifacts based on their statistical properties in the component subspace. We adapted the existing ASR implementation by using Riemannian geometry for covariance matrix processing. EEG data that were recorded on smartphone in both outdoors and indoors conditions were used for evaluation (N = 27). A direct comparison between the original ASR and Riemannian ASR (rASR) was conducted for three performance measures: reduction of eye-blinks (sensitivity), improvement of visual-evoked potentials (VEPs) (specificity), and computation time (efficiency). Compared to ASR, our rASR algorithm performed favorably on all three measures. We conclude that rASR is suitable for the offline and online correction of multichannel EEG data acquired in laboratory and in field conditions

Identification of superior reference genes for data normalisation of expression studies via quantitative PCR in hybrid roses (Rosa hybrida)

<p>Abstract</p> <p>Background</p> <p>Gene expression studies are a prerequisite for understanding the biological function of genes. Because of its high sensitivity and easy use, quantitative PCR (qPCR) has become the gold standard for gene expression quantification. To normalise qPCR measurements between samples, the most prominent technique is the use of stably expressed endogenous control genes, the so called reference genes. However, recent studies show there is no universal reference gene for all biological questions. Roses are important ornamental plants for which there has been no evaluation of useful reference genes for gene expression studies.</p> <p>Results</p> <p>We used three different algorithms (BestKeeper, geNorm and NormFinder) to validate the expression stability of nine candidate reference genes in different rose tissues from three different genotypes of <it>Rosa hybrida </it>and in leaves treated with various stress factors. The candidate genes comprised the classical "housekeeping genes" (<it>Actin, EF-1α, GAPDH</it>, <it>Tubulin </it>and <it>Ubiquitin</it>), and genes showing stable expression in studies in <it>Arabidopsis </it>(<it>PP2A, SAND, TIP </it>and <it>UBC</it>). The programs identified no single gene that showed stable expression under all of the conditions tested, and the individual rankings of the genes differed between the algorithms. Nevertheless the new candidate genes, specifically, <it>PP2A </it>and <it>UBC</it>, were ranked higher as compared to the other traditional reference genes. In general, <it>Tubulin </it>showed the most variable expression and should be avoided as a reference gene.</p> <p>Conclusions</p> <p>Reference genes evaluated as suitable in experiments with <it>Arabidopsis thaliana </it>were stably expressed in roses under various experimental conditions. In most cases, these genes outperformed conventional reference genes, such as <it>EF1-α </it>and <it>Tubulin</it>. We identified <it>PP2A</it>, <it>SAND </it>and <it>UBC </it>as suitable reference genes, which in different combinations may be used for normalisation in expression analyses via qPCR for different rose tissues and stress treatments. However, the vast genetic variation found within the genus <it>Rosa</it>, including differences in ploidy levels, might also influence expression stability of reference genes, so that future research should also consider different genotypes and ploidy levels.</p

EEG-fMRI Based Information Theoretic Characterization of the Human Perceptual Decision System

The modern metaphor of the brain is that of a dynamic information processing device. In the current study we investigate how a core cognitive network of the human brain, the perceptual decision system, can be characterized regarding its spatiotemporal representation of task-relevant information. We capitalize on a recently developed information theoretic framework for the analysis of simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging data (fMRI) (Ostwald et al. (2010), NeuroImage 49: 498–516). We show how this framework naturally extends from previous validations in the sensory to the cognitive domain and how it enables the economic description of neural spatiotemporal information encoding. Specifically, based on simultaneous EEG-fMRI data features from n = 13 observers performing a visual perceptual decision task, we demonstrate how the information theoretic framework is able to reproduce earlier findings on the neurobiological underpinnings of perceptual decisions from the response signal features' marginal distributions. Furthermore, using the joint EEG-fMRI feature distribution, we provide novel evidence for a highly distributed and dynamic encoding of task-relevant information in the human brain

Human Computer Interaction Meets Psychophysiology: A Critical Perspective

Human computer interaction (HCI) groups are more and more often exploring the utility of new, lower cost electroencephalography (EEG) interfaces for assessing user engagement and experience as well as for directly controlling computers. While the potential benefits of using EEG are considerable, we argue that research is easily driven by what we term naïve neurorealism. That is, data obtained with psychophysiological devices have poor reliability and uncertain validity, making inferences on mental states difficult. This means that unless sufficient care is taken to address the inherent shortcomings, the contributions of psychophysiological human computer interaction are limited to their novelty value rather than bringing scientific advance. Here, we outline the nature and severity of the reliability and validity problems and give practical suggestions for HCI researchers and reviewers on the way forward, and which obstacles to avoid. We hope that this critical perspective helps to promote good practice in the emerging field of psychophysiology in HCI

NeuroPlace: categorizing urban places according to mental states

Urban spaces have a great impact on how people’s emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture

How a co-actor’s task affects monitoring of own errors: evidence from a social event-related potential study

Efficient flexible behavior requires continuous monitoring of performance for possible deviations from the intended goal of an action. This also holds for joint action. When jointly performing a task, one needs to not only know the other’s goals and intentions but also generate behavioral adjustments that are dependent on the other person’s task. Previous studies have shown that in joint action people not only represent their own task but also the task of their co-actor. The current study investigated whether these so-called shared representations affect error monitoring as reflected in the response-locked error-related negativity (Ne/ERN) following own errors. Sixteen pairs of participants performed a social go/no-go task, while EEG and behavioral data were obtained. Responses were compatible or incompatible relative to the go/no-go action of the co-actor. Erroneous responses on no-go stimuli were examined. The results demonstrated increased Ne/ERN amplitudes and longer reaction times following errors on compatible compared to incompatible no-go stimuli. Thus, Ne/ERNs were larger after errors on trials that did not require a response from the co-actor either compared to errors on trials that did require a response from the co-actor. As the task of the other person is the only difference between these two types of errors, these findings show that people also represent their co-actor’s task during error monitoring in joint action. An extension of existing models on performance monitoring in individual action is put forward to explain the current findings in joint action. Importantly, we propose that inclusion of a co-actor’s task in performance monitoring may facilitate adaptive behavior in social interactions enabling fast anticipatory and corrective actions

Working memory capacity modulates habituation rate: Evidence from a cross-modal auditory distraction paradigm

Habituation of the orienting response is a pivotal part of selective attention, and previous research has related working memory capacity (WMC) to attention control. Against this background, the purpose of this study was to investigate whether individual differences in WMC contribute to habituation rate. The participants categorized visual targets across six blocks of trials. Each target was preceded either by a standard sound or, on rare trials, by a deviant. The magnitude of the deviation effect (i.e., prolonged response time when the deviant was presented) was relatively large in the beginning but attenuated toward the end. There was no relationship between WMC and the deviation effect at the beginning, but there was at the end, and greater WMC was associated with greater habituation. These results indicate that high memory ability increases habituation rate, and they support theories proposing a role for cognitive control in habituation and in some forms of auditory distraction

Aging and Error Processing: Age Related Increase in the Variability of the Error-Negativity Is Not Accompanied by Increase in Response Variability

Background: Several studies report an amplitude reduction of the error negativity (Ne or ERN), an event-related potential occurring after erroneous responses, in older participants. In earlier studies it was shown that the Ne can be explained by a single independent component. In the present study we aimed to investigate whether the Ne reduction usually found in older subjects is due to an altered component structure, i.e., a true alteration in response monitoring in older subjects. Methodology/Principal Findings: Two age groups conducted two tasks with different stimulus response mappings and task difficulty. Both groups received fully balanced speed or accuracy instructions and an individually adapted deadline in both tasks. Event-related potentials, Independent Component analysis of EEG-data and between trial variability of the Ne were combined with analysis of error rates, coefficients of variation of RT-data and ex-Gaussian fittings to reaction times. The Ne was examined by means of ICA and PCA, yielding a prominent independent component on error trials, the Ne-IC. The Ne-IC was smaller in the older than the younger subjects for both speed and accuracy instructions. Also, the Ne-IC contributed to a much lesser extent to the Ne in older than in younger subjects. RT distribution parameters were not related to Ne/ERP-variability. Conclusions/Significance: The results show a genuine reduction as well as a different component structure of the Ne in older compared to young subjects. This reduction is not reflected in behaviour, apart from a general slowing of olde