The Impact of Flow in an EEG-based Brain Computer Interface

Major issues in Brain Computer Interfaces (BCIs) include low usability and poor user performance. This paper tackles them by ensuring the users to be in a state of immersion, control and motivation, called state of flow. Indeed, in various disciplines, being in the state of flow was shown to improve performances and learning. Hence, we intended to draw BCI users in a flow state to improve both their subjective experience and their performances. In a Motor Imagery BCI game, we manipulated flow in two ways: 1) by adapting the task difficulty and 2) by using background music. Results showed that the difficulty adaptation induced a higher flow state, however music had no effect. There was a positive correlation between subjective flow scores and offline performance, although the flow factors had no effect (adaptation) or negative effect (music) on online performance. Overall, favouring the flow state seems a promising approach for enhancing users' satisfaction, although its complexity requires more thorough investigations

Theoretical analysis of xDAWN algorithm: application to an efficient sensor selection in a P300 BCI

International audienceA Brain-Computer Interface (BCI) is a specific type of human-machine interface that enables communication between a subject/patient and a computer by a direct control from the decoding of brain activity. To improve the ergonomics and to minimize the cost of such a BCI, reducing the number of electrodes is mandatory. A theoretical analysis of the subjacent model induced by the BCI paradigm leads to derive a closed form theoretical expression of the spatial filters which maximize the signal to signal-plus-noise ratio. Moreover, this new formulation is useful to improve a previously introduced method to automatically select relevant sensors. Experimental results on 20 subjects show that the proposed method is efficient to select the most relevant sensors: from 32 down to 8 sensors, the loss in classification accuracy is less than 2%. Furthermore, the computational time required to rank the 32 sensors is reduced by a 4.6 speed up factor allowing dynamical monitoring of sensor relevance as a marker of the user's mental state

Analyse discriminante matricielle descriptive. Application a l'\'etude de signaux EEG

We focus on the descriptive approach to linear discriminant analysis for matrix-variate data in the binary case. Under a separability assumption on row and column variability, the most discriminant linear combinations of rows and columns are determined by the singular value decomposition of the difference of the class-averages with the Mahalanobis metric in the row and column spaces. This approach provides data representations of data in two-dimensional or three-dimensional plots and singles out discriminant components. An application to electroencephalographic multi-sensor signals illustrates the relevance of the method.Comment: in French, Journ{\'e}es de statistique de la SFDS, Jun 2015, Lille, Franc

Hearing faces: how the infant brain matches the face it sees with the speech it hears

Speech is not a purely auditory signal. From around 2 months of age, infants are able to correctly match the vowel they hear with the appropriate articulating face. However, there is no behavioral evidence of integrated audiovisual perception until 4 months of age, at the earliest, when an illusory percept can be created by the fusion of the auditory stimulus and of the facial cues (McGurk effect). To understand how infants initially match the articulatory movements they see with the sounds they hear, we recorded high-density ERPs in response to auditory vowels that followed a congruent or incongruent silently articulating face in 10-week-old infants. In a first experiment, we determined that auditory–visual integration occurs during the early stages of perception as in adults. The mismatch response was similar in timing and in topography whether the preceding vowels were presented visually or aurally. In the second experiment, we studied audiovisual integration in the linguistic (vowel perception) and nonlinguistic (gender perception) domain. We observed a mismatch response for both types of change at similar latencies. Their topographies were significantly different demonstrating that cross-modal integration of these features is computed in parallel by two different networks. Indeed, brain source modeling revealed that phoneme and gender computations were lateralized toward the left and toward the right hemisphere, respectively, suggesting that each hemisphere possesses an early processing bias. We also observed repetition suppression in temporal regions and repetition enhancement in frontal regions. These results underscore how complex and structured is the human cortical organization which sustains communication from the first weeks of life on

Analyse discriminante matricielle descriptive. Application a l'étude de signaux EEG

National audienceWe focus on the descriptive approach to linear discriminant analysis for matrix-variate data in the binary case. Under a separability assumption on row and column variability, the most discriminant linear combinations of rows and columns are determined by the singular value decomposition of the difference of the class-averages with the Mahalanobis metric in the row and column spaces. This approach provides data representations of data in two-dimensional or three-dimensional plots and singles out discriminant components. An application to electroencephalographic multi-sensor signals illustrates the relevance of the method.Nous nous intéressons à l'approche descriptive de l'analyse discriminante linéaire de données matricielles dans le cas binaire. Sous l'hypothèse de séparabilité de la variabilité des lignes de celle des colonnes, les combinaisons linéaires des lignes et des colonnes les plus discriminantes sont déterminées par la décomposition en valeurs singulières de la différence des moyennes des deux classes en munissant les espaces des lignes et des colonnes de la métrique de Mahalanobis. Cette approche permet d'obtenir des représentations des données dans des plans factoriels et de dégager des composantes discriminantes. Une application a des signaux d'électroencéphalographie multi-capteurs illustre la pertinence de la méthode

Effect of Membrane Exposure on Guided Bone Regeneration: A Systematic Review and Meta‐Analysis

Aims: This review aimed at investigating the effect of membrane exposure on guided bone regeneration (GBR) outcomes at peri-implant sites and edentulous ridges. Material and Methods: Electronic and manual literature searches were conducted by two independent reviewers using four databases, including MEDLINE, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials, for articles up to February 2017. Articles were included if they were human clinical trials or case series reporting outcomes of GBR procedures with and without membrane exposure. A random-effects meta-analysis was conducted, and the weighted mean difference (WMD) between the two groups and 95% confidence interval (CI) were reported. Results: Overall, eight articles were included in the quantitative analysis. The WMD of the horizontal bone gain at edentulous ridges was −76.24% (95% CI = −137.52% to −14.97%, p = .01) between sites with membrane exposure and without exposure. In addition, the WMD of the dehiscence reduction at peri- implant sites was −27.27% (95% CI of −45.87% to −8.68%, p = .004). Both analyses showed significantly favorable outcomes at the sites without membrane exposure. Conclusion: Based on the findings of this study, membrane exposure after GBR procedures has a significant detrimental influence on the outcome of bone augmentation. For the edentulous ridges, the sites without membrane exposure achieved 74% more horizontal bone gain than the sites with exposure. For peri-implant dehiscence defects, the sites without membrane exposure had 27% more defect reduction than the sites with exposure

Mismatch Negativity: time for deconstruction

Error signals are the cornerstone of predictive coding and are widely considered essential to sensory perception and beyond. The mismatch negativity (MMN) is arguably the most emblematic and most studied brain error signal. It is affected in many brain disorders. However, its precise algorithmic function and the underlying physiology remain mysterious. Over the past decade, theoretical and computational explanations have been put forward. They highlight a paradox: the MMN is considered a signature of context-dependent perceptual learning, although it is defined as an evoked response averaged across trials, thus neglecting the information carried by error signal fluctuations over time. We propose to deconstruct the MMN, by virtue of hypothesis driven computational approaches whose aim it to account for these fluctuations

Lamin A/C sustains PcG protein architecture, maintaining transcriptional repression at target genes

Beyond its role in providing structure to the nuclear envelope, lamin A/C is involved in transcriptional regulation. However, its cross talk with epigenetic factors--and how this cross talk influences physiological processes--is still unexplored. Key epigenetic regulators of development and differentiation are the Polycomb group (PcG) of proteins, organized in the nucleus as microscopically visible foci. Here, we show that lamin A/C is evolutionarily required for correct PcG protein nuclear compartmentalization. Confocal microscopy supported by new algorithms for image analysis reveals that lamin A/C knock-down leads to PcG protein foci disassembly and PcG protein dispersion. This causes detachment from chromatin and defects in PcG protein-mediated higher-order structures, thereby leading to impaired PcG protein repressive functions. Using myogenic differentiation as a model, we found that reduced levels of lamin A/C at the onset of differentiation led to an anticipation of the myogenic program because of an alteration of PcG protein-mediated transcriptional repression. Collectively, our results indicate that lamin A/C can modulate transcription through the regulation of PcG protein epigenetic factors

Canonical Source Reconstruction for MEG

We describe a simple and efficient solution to the problem of reconstructing electromagnetic sources into a canonical or standard anatomical space. Its simplicity rests upon incorporating subject-specific anatomy into the forward model in a way that eschews the need for cortical surface extraction. The forward model starts with a canonical cortical mesh, defined in a standard stereotactic space. The mesh is warped, in a nonlinear fashion, to match the subject's anatomy. This warping is the inverse of the transformation derived from spatial normalization of the subject's structural MRI image, using fully automated procedures that have been established for other imaging modalities. Electromagnetic lead fields are computed using the warped mesh, in conjunction with a spherical head model (which does not rely on individual anatomy). The ensuing forward model is inverted using an empirical Bayesian scheme that we have described previously in several publications. Critically, because anatomical information enters the forward model, there is no need to spatially normalize the reconstructed source activity. In other words, each source, comprising the mesh, has a predetermined and unique anatomical attribution within standard stereotactic space. This enables the pooling of data from multiple subjects and the reporting of results in stereotactic coordinates. Furthermore, it allows the graceful fusion of fMRI and MEG data within the same anatomical framework

Dynamic Causal Modeling of Preclinical Autosomal-Dominant Alzheimer’s Disease

Dynamic causal modeling (DCM) is a framework for making inferences about changes in brain connectivity using neuroimaging data. We fitted DCMs to high-density EEG data from subjects performing a semantic picture matching task. The subjects are carriers of the PSEN1 mutation, which leads to early onset Alzheimer’s disease, but at the time of EEG acquisition in 1999, these subjects were cognitively unimpaired. We asked 1) what is the optimal model architecture for explaining the event-related potentials in this population, 2) which connections are different between this Presymptomatic Carrier (PreC) group and a Non-Carrier (NonC) group performing the same task, and 3) which network connections are predictive of subsequent Mini-Mental State Exam (MMSE) trajectories. We found 1) a model with hierarchical rather than lateral connections between hemispheres to be optimal, 2) that a pathway from right inferotemporal cortex (IT) to left medial temporal lobe (MTL) was preferentially activated by incongruent items for subjects in the PreC group but not the NonC group, and 3) that increased effective connectivity among left MTL, right IT, and right MTL was predictive of subsequent MMSE scores