Classifying regularized sensor covariance matrices: An alternative to CSP

Item does not contain fulltextCommon spatial patterns ( CSP) is a commonly used technique for classifying imagined movement type brain-computer interface ( BCI) datasets. It has been very successful with many extensions and improvements on the basic technique. However, a drawback of CSP is that the signal processing pipeline contains two supervised learning stages: the first in which class-relevant spatial filters are learned and a second in which a classifier is used to classify the filtered variances. This may lead to potential overfitting issues, which are generally avoided by limiting CSP to only a few filters. This work argues for an alternative approach where only a single supervised learning stage is needed. The key step in this approach is to use whitened spatial covariance matrices as features and to use a linear classifier to simultaneously learn the spatial filters and the classifier weights. Unfortunately, this approach can also lead to overfitting problems. We show how these problems can be addressed by appropriately regularizing the whitening computation. Ridge regularized covariance classification outperforms whitened spatial covariance, CSP, and two regularized CSP classification methods on an imagined movement dataset indicating the usefulness of this regularization method for BCI. Trace norm regularization can help with the interpretability of the results.8 p

BCI and a user's judgment of agency

Item does not contain fulltextPerforming an action with the assistance of a BCI may affect a user's judgment of agency, resulting in an illusion of control, or automatism. We analyze this possibility from a theoretical perspective and discuss various factors that might influence a user's judgment of agency in a BCI context. We present two pilot experiments that illustrate how this theoretical possibility can be investigated experimentally. We examine potential psychological, ethical, and legal implications of mistaken judgments, and potential benefits of the constructive manipulation of a user's judgment of agency

Exploring the impact of target eccentricity and task difficulty on covert visual spatial attention and its implications for brain computer interfacing

Objective Covert visual spatial attention is a relatively new task used in brain computer interfaces (BCIs) and little is known about the characteristics which may affect performance in BCI tasks. We investigated whether eccentricity and task difficulty affect alpha lateralization and BCI performance. Approach We conducted a magnetoencephalography study with 14 participants who performed a covert orientation discrimination task at an easy or difficult stimulus contrast at either a near (3.5°) or far (7°) eccentricity. Task difficulty was manipulated block wise and subjects were aware of the difficulty level of each block. Main Results Grand average analyses revealed a significantly larger hemispheric lateralization of posterior alpha power in the difficult condition than in the easy condition, while surprisingly no difference was found for eccentricity. The difference between task difficulty levels was significant in the interval between 1.85 s and 2.25 s after cue onset and originated from a stronger decrease in the contralateral hemisphere. No significant effect of eccentricity was found. Additionally, single-trial classification analysis revealed a higher classification rate in the difficult (65.9%) than in the easy task condition (61.1%). No effect of eccentricity was found in classification rate. Significance Our results indicate that manipulating the difficulty of a task gives rise to variations in alpha lateralization and that using a more difficult task improves covert visual spatial attention BCI performance. The variations in the alpha lateralization could be caused by different factors such as an increased mental effort or a higher visual attentional demand. Further research is necessary to discriminate between them. We did not discover any effect of eccentricity in contrast to results of previous research

BCI and a user's judgment of agency

Performing an action with the assistance of a BCI may affect a user's judgment of agency, resulting in an illusion of control, or automatism. We analyze this possibility from a theoretical perspective and discuss various factors that might influence a user's judgment of agency in a BCI context. We present two pilot experiments that illustrate how this theoretical possibility can be investigated experimentally. We examine potential psychological, ethical, and legal implications of mistaken judgments, and potential benefits of the constructive manipulation of a user's judgment of agency

Looking around with your brain in a virtual world

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