Advances in sensing and satellite technologies and the growth of Internet have resulted in a vast amount of uncertain spatial data. Extracting interaction patterns from these uncertain data is a challenging task. In this paper, we propose to model the spatial features in a continuous space through the use of influence functions. For each feature type, we build an influence map that captures the...

the venue for interaction a virtual online data super-store. Additionally, the accessibility to data is no longer tied to ownership or control of the acquiring technology, subject to considerable government restriction, or restricted to only those entities that can invest in considerable computer storage. In recent years, several private companies have built the requisite infrastructure to make...

In looking at ecological processes, interest in the pattern of occurrences of species, e.g., the pattern of trees in a forest, say junipers and pinions. In spatial epidemiology, we seek to find pattern in disease cases, perhaps different patterns for cases vs. controls. Exple: breast cancer cases; treatment option mastectomy or radiation In syndromic surveillance we seek to identify disease out...

The formation of spatial genetic structure (SGS) may originate from different patterns of seed deposition in the landscape, and is mostly determined by seed dispersal limitation. After dispersal, mechanisms such as filtering by environmental factors or attack by herbivores/pathogens throughout plant development stages, and potentially either disrupt or intensify SGS patterns. We investigated ho...

Non-stationarities in EEG signals coming from electrode artefacts, muscular activity or changes of task involvement can negatively affect the classification accuracy of Brain-Computer Interface (BCI) systems. In this paper we investigate three methods to alleviate this: (1) Regularization of Common Spatial Patterns (CSP) towards stationary subspaces in order to reduce the influence of artefacts...

When we want to use brain-computer interfaces (BCI) as an input modality for gaming, a short setup procedure is necessary. Therefore a user model has to be learned using small training sets. The common spatial patterns (CSP) algorithm is often used in BCI. In this work we investigate how the CSP algorithm generalizes when using small training sets, how the performance changes over time, and how...

In this paper we propose, describe, and evaluate a novel deep learning method for classifying binary motor imagery data. This model is designed to perform CSP-like feature extractions. It can be seen as a neural network with a specifically designed architecture where the latent space corresponds naturally to the features found in CSP methods. Our model allows for easy generalization from spatia...

During the last years interest has been growing to find an effective communication channel which translates human intentions into control signals for a computer, the so called Brain-Computer Interface (BCI). One main goal of research is to help patients with severe neuromuscular disabilities by substituting normal motor outputs. Various cortical processes were identified which are suitable for ...

Common spatial pattern (CSP) is one of the most popular and effective feature extraction methods for motor imagery-based brain-computer interface (BCI), but the inherent drawback of CSP is that the estimation of the covariance matrices is sensitive to noise. In this work, local temporal correlation (LTC) information was introduced to further improve the covariance matrices estimation (LTCCSP). ...

Brain computer interfaces (BCI) create a new approach to human computer communication, allowing the user to control a system simply by performing mental tasks such as motor imagery. This paper proposes and analyses different strategies for time segmentation in extracting common spatial patterns of the brain signals associated to these tasks leading to an improvement of BCI performance.