An important goal in Brain-Computer Interfacing (BCI) is to find and enhance procedural strategies for users for whom BCI control is not sufficiently accurate. To address this challenge, we conducted ...offline analyses and online experiments to test whether the classification of different types of motor imagery could be improved when the training of the classifier was performed on the data obtained with the assistive muscular stimulation below the motor threshold. 10 healthy participants underwent three different types of experimental conditions: a) Motor imagery (MI) of hands and feet b) sensory threshold neuromuscular electrical stimulation (STM) of hands and feet while resting and c) sensory threshold neuromuscular electrical stimulation during performance of motor imagery (BOTH). Also, another group of 10 participants underwent conditions a) and c). Then, online experiments with 15 users were performed. These subjects received neurofeedback during MI using classifiers calibrated either on MI or BOTH data recorded in the same experiment. Offline analyses showed that decoding MI alone using a classifier based on BOTH resulted in a better BCI accuracy compared to using a classifier based on MI alone. Online experiments confirmed accuracy improvement of MI alone being decoded with the classifier trained on BOTH data. In addition, we observed that the performance in MI condition could be predicted on the basis of a more pronounced connectivity within sensorimotor areas in the frequency bands providing the best performance in BOTH. These finding might offer a new avenue for training SMR-based BCI systems particularly for users having difficulties to achieve efficient BCI control. It might also be an alternative strategy for users who cannot perform real movements but still have remaining afferent pathways (e.g., ALS and stroke patients).
•Afferent stimulation (STM) in the calibration phase was used to enhance BCI performance.•Concurrent motor imagery and STM had stronger modulation of sensorimotor oscillations.•STM significantly improved BCI accuracy particularly for poorly performing subjects.•Classifiers trained with STM can be successfully used online even without stimulation.•These findings ease the practical applicability of STM-based BCI systems.
Abstract Objective Internet addiction becomes a growing health problem worldwide with prevalence rates up to 3%. Still, uncertainties exist regarding its diagnostics and clinical characterization. ...Especially the lacking clinical evidence regarding self-report measures assessing Internet addiction has been criticized. Methods This study aimed to characterize 290 German treatment seekers and to determine the diagnostic accuracy of a self-report scale for Internet addiction. Patients filled in self-report measures (SCL-90R, PHQ, AICA-S – Scale for the Assessment of Internet and Computer game Addiction) and underwent diagnostic interviews to assess symptoms of Internet addiction and level of functioning. Results Of the predominantly male treatment seekers 71% met the clinical diagnosis of Internet addiction. These displayed higher levels of psychopathology, especially depressive and dissociative symptoms. Half of the patients met criteria for one further psychiatric disorder according to clinical interviews, especially depressive disorders. Their level of functioning was decreased in all domains. AICA-S showed good psychometric properties and satisfying diagnostic accuracy (sensitivity: 80.5%; specificity: 82.4%). Discussion In this sample, Internet addiction was associated with high levels of psychosocial distress that is mainly related to depressive symptoms. Co-morbid disorders were common among those patients. First analyses on diagnostic accuracy of AICA-S (using the therapist's rating on Internet addiction as an independent external criterion) showed promising results.
We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar “estimator plus and minus a standard error times a critical ...value” form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given “worst‐case” spatial correlation model. The critical value is chosen to ensure coverage in a benchmark parametric model for the spatial correlations. The method is shown to control coverage in finite sample Gaussian settings in a restricted but nonparametric class of models and in large samples whenever the spatial correlation is weak, that is, with average pairwise correlations that vanish as the sample size gets large. We also provide results on the efficiency of the method.
Applied work routinely relies on heteroscedasticity and autocorrelation consistent (HAC) standard errors when conducting inference in a time series setting. As is well known, however, these ...corrections perform poorly in small samples under pronounced autocorrelations. In this article, I first provide a review of popular methods to clarify the reasons for this failure. I then derive inference that remains valid under a specific form of strong dependence. In particular, I assume that the long-run properties can be approximated by a stationary Gaussian AR(1) model, with coefficient arbitrarily close to one. In this setting, I derive tests that come close to maximizing a weighted average power criterion. Small sample simulations show these tests to perform well, also in a regression context.
Binding bacteria: Discotic molecules self-assemble into columnar supramolecular polymers that show strong polyvalent binding to bacteria by virtue of mannose ligands attached at their periphery ...(orange; see picture). The reversible formation of the supramolecular polymers allows simple mixing of differently substituted monomers and the optimization of bacterial aggregation.
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