Classification of P300 component using a riemannian ensemble approach

We present a framework for P300 ERP classification on the 2019 IFMBE competition dataset using a combination of a Riemannian geometry and ensemble learning. Covariance matrices and ERP prototypes are extracted after the EEG is passed through a filter bank and an ensemble of LDA classifiers is trained on subsets of channels, trials, and frequencies. The model selects a final class based on maximum probability of evidence from all ensembles. Our pipeline achieves an average classification accuracy of 81.2% on the test set

Improving SNR and reducing training time of classifiers in large datasets via kernel averaging

Kernel methods are of growing importance in neuroscience research. As an elegant extension of linear methods, they are able to model complex non-linear relationships. However, since the kernel matrix grows with data size, the training of classifiers is computationally demanding in large datasets. Here, a technique developed for linear classifiers is extended to kernel methods: In linearly separable data, replacing sets of instances by their averages improves signal-to-noise ratio (SNR) and reduces data size. In kernel methods, data is linearly non-separable in input space, but linearly separable in the high-dimensional feature space that kernel methods implicitly operate in. It is shown that a classifier can be efficiently trained on instances averaged in feature space by averaging entries in the kernel matrix. Using artificial and publicly available data, it is shown that kernel averaging improves classification performance substantially and reduces training time, even in non-linearly separable data

The Role of Attention in Ambiguous Reversals of Structure-From-Motion

Multiple dots moving independently back and forth on a flat screen induce a compelling illusion of a sphere rotating in depth (structure-from-motion). If all dots simultaneously reverse their direction of motion, two perceptual outcomes are possible: either the illusory rotation reverses as well (and the illusory depth of each dot is maintained), or the illusory rotation is maintained (but the illusory depth of each dot reverses). We investigated the role of attention in these ambiguous reversals. Greater availability of attention – as manipulated with a concurrent task or inferred from eye movement statistics – shifted the balance in favor of reversing illusory rotation (rather than depth). On the other hand, volitional control over illusory reversals was limited and did not depend on tracking individual dots during the direction reversal. Finally, display properties strongly influenced ambiguous reversals. Any asymmetries between ‘front’ and ‘back’ surfaces – created either on purpose by coloring or accidentally by random dot placement – also shifted the balance in favor of reversing illusory rotation (rather than depth). We conclude that the outcome of ambiguous reversals depends on attention, specifically on attention to the illusory sphere and its surface irregularities, but not on attentive tracking of individual surface dots

The hippocampus as the switchboard between perception and memory

Adaptive memory recall requires a rapid and flexible switch from external perceptual reminders to internal mnemonic representations. However, owing to the limited temporal or spatial resolution of brain imaging modalities used in isolation, the hippocampal–cortical dynamics supporting this process remain unknown. We thus employed an object-scene cued recall paradigm across two studies, including intracranial electroencephalography (iEEG) and high-density scalp EEG. First, a sustained increase in hippocampal high gamma power (55 to 110 Hz) emerged 500 ms after cue onset and distinguished successful vs. unsuccessful recall. This increase in gamma power for successful recall was followed by a decrease in hippocampal alpha power (8 to 12 Hz). Intriguingly, the hippocampal gamma power increase marked the moment at which extrahippocampal activation patterns shifted from perceptual cue toward mnemonic target representations. In parallel, source-localized EEG alpha power revealed that the recall signal progresses from hippocampus to posterior parietal cortex and then to medial prefrontal cortex. Together, these results identify the hippocampus as the switchboard between perception and memory and elucidate the ensuing hippocampal–cortical dynamics supporting the recall process

Concept-level design analytics for blended courses

Comunicació presentada a: EC-TEL 2019 celebrat a Delft, Països Baixos, del 16 al 19 de setembre de 2019.Although many efforts are being made to provide educators with dashboards and tools to understand student behaviors within specific technological environments (learning analytics), there is a lack of work in supporting educators in making data-informed design decisions when designing a blended course and planning learning activities. In this paper, we introduce concept-level design analytics, a knowledge-based visualization, which uncovers facets of the learning activities that are being authored. The visualization is integrated into a (blended) learning design authoring tool, edCrumble. This new approach is explored in the context of a higher education programming course, where teaching assistants design labs and home practice sessions with online smart learning content on a weekly basis. We performed a within-subjects user study to compare the use of the design tool both with and without the visualization. We studied the differences in terms of cognitive load, design outcomes and user actions within the system to compare both conditions to the objective of evaluating the impact of using design analytics during the decision-making phase of course design.This work has also been partially funded by NSF DRL 1740775, “la Caixa Foundation” (CoT project, 100010434) and FEDER, the National Research Agency of the Spanish Ministry of Science, Innovations and Universities MDM-2015-0502, TIN2014-53199-C3-3-R, TIN2017-85179-C3-3-R. DHL is a Serra Húnter Fellow