Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification

Objective. The main goal of this work is to develop a model for multi-sensor signals such as MEG or EEG signals, that accounts for the inter-trial variability, suitable for corresponding binary classification problems. An important constraint is that the model be simple enough to handle small size and unbalanced datasets, as often encountered in BCI type experiments. Approach. The method involves linear mixed effects statistical model, wavelet transform and spatial filtering, and aims at the characterization of localized discriminant features in multi-sensor signals. After discrete wavelet transform and spatial filtering, a projection onto the relevant wavelet and spatial channels subspaces is used for dimension reduction. The projected signals are then decomposed as the sum of a signal of interest (i.e. discriminant) and background noise, using a very simple Gaussian linear mixed model. Main results. Thanks to the simplicity of the model, the corresponding parameter estimation problem is simplified. Robust estimates of class-covariance matrices are obtained from small sample sizes and an effective Bayes plug-in classifier is derived. The approach is applied to the detection of error potentials in multichannel EEG data, in a very unbalanced situation (detection of rare events). Classification results prove the relevance of the proposed approach in such a context. Significance. The combination of linear mixed model, wavelet transform and spatial filtering for EEG classification is, to the best of our knowledge, an original approach, which is proven to be effective. This paper improves on earlier results on similar problems, and the three main ingredients all play an important role

Distributional reaction time properties in the Eriksen task: marked differences or hidden similarities with the Simon task?

In conflict tasks, the irrelevant stimulus attribute needs to be suppressed for the correct response to be produced. In the Simon task, earlier researchers have proposed that this suppression is the reason that, after an initial increase, the interference effect decreases for longer RTs, as reflected by late, negative-going delta plots. This view has been challenged by observations of positive-going delta plots, even for long RTs, in other conflict tasks, despite a similar necessity for suppression. For late negative-going delta plots to be interpreted as reflecting suppression, a necessary, although maybe not sufficient, condition is that similar patterns should be observed for other conflict tasks. We reasoned that a similar suppression could be present, but hidden, in the Eriksen flanker task. By recording and analyzing electromyograms of the muscles involved in response execution, we could compute delta plots separately for trials that elicited a subthreshold incorrect response activation (partial error). Late negative-going delta plots were observable on partial-error trials, although they were weaker than for the Simon task, reducing the impact of this inversion on the overall distribution. We further showed that this pattern is modulated by time pressure. Those results indicate that mechanisms leading to negative-going delta plots, similar to those observed in the Simon task, are also at play in the Eriksen task. The link between negative-going delta plots and executive online control is discussed

Identification of Burgers vectors along <111> in In-doped GaAs, by X-ray transmission topography andimage simulation.

International audienceLong dislocations with Burgers vectors along are unusual in f.c.c. lattices. X-ray topographs have beenobtained of as-grown GaAs crystals doped with 1020 atoms cm -3 of In, where the usual extinction criterion g.b = 0leads to this type of defect. However, for several g satisfying the condition g.b = 0 with b = a [111], the images of thesedislocations were still clearly visible. Comparison between experimental and computer-simulated X-ray topographicsections of these defects confirms the existence of Burgers vectors along

Spatial and temporal resolutions of EEG: Is it really black and white? A scalp current density view

Among the different brain imaging techniques, electroencephalography (EEG) is classically considered as having an excellent temporal resolution, but a poor spatial one. Here, we argue that the actual temporal resolution of conventional (scalp potentials) EEG is overestimated, and that volume conduction, the main cause of the poor spatial resolution of EEG, also distorts the recovered time course of the underlying sources at scalp level, and hence degrades the actual temporal resolution of EEG. While Current Source Density (CSD) estimates, through the Surface Laplacian (SL) computation, are well known to dramatically reduce volume conduction effects and hence improve EEG spatial resolution, its positive impact on EEG temporal resolution is much less recognized. In two simulation studies, we first show how volume conduction and reference electrodes distort the scalp potential time course, and how SL transform provides a much better spatio-temporal description. We then exemplify similar effects on two empirical datasets. We show how the time courses of the scalp potentials mis-estimate the latencies of the relevant brain events and that CSD provides a much richer, and much more accurate, view of the spatio-temporal dynamics of brain activity

Controlling Your Impulses: Electrical Stimulation of the Human Supplementary Motor Complex Prevents Impulsive Errors

To err is human. However, an inappropriate urge does not always result in error. Impulsive errors thus entail both a motor system capture by an urge to act and a failed inhibition of that impulse. Here we show that neuromodulatory electrical stimulation of the supplementary motor complex in healthy humans leaves action urges unchanged but prevents them from turning into overt errors. Subjects performed a choice reaction-time task known to trigger impulsive responses, leading to fast errors that can be revealed by analyzing accuracy as a function of poststimulus time. Yet, such fast errors are only the tip of the iceberg: electromyography (EMG) revealed fast subthreshold muscle activation in the incorrect response hand in an even larger proportion of overtly correct trials, revealing covert response impulses not discernible in overt behavior. Analyzing both overt and covert response tendencies enables to gauge the ability to prevent these incorrect impulses from turning into overt action errors. Hyperpolarizing the supplementary motor complex using transcranial direct current stimulation (tDCS) preserves action impulses but prevents their behavioral expression. This new combination of detailed behavioral, EMG, and tDCS techniques clarifies the neurophysiology of impulse control, and may point to avenues for improving impulse control deficits in various neurologic and psychiatric disorders