An uncued brain-computer interface using reservoir computing

Brain-Computer Interfaces are an important and promising avenue for possible next-generation assistive devices. In this article, we show how Reservoir Comput- ing – a computationally efficient way of training recurrent neural networks – com- bined with a novel feature selection algorithm based on Common Spatial Patterns can be used to drastically improve performance in an uncued motor imagery based Brain-Computer Interface (BCI). The objective of this BCI is to label each sample of EEG data as either motor imagery class 1 (e.g. left hand), motor imagery class 2 (e.g. right hand) or a rest state (i.e., no motor imagery). When comparing the re- sults of the proposed method with the results from the BCI Competition IV (where this dataset was introduced), it turns out that the proposed method outperforms the winner of the competition

Architectural Information Modelling in Construction History

The past few years show a significant increase in the usage of three-dimensional modelling and semantic description techniques for architectural research purposes. Where this increase has already shaped today’s design and construction industry, research in architectural and construction history can still improve its work methods and results through these techniques. Therefore, we propose a new conceptual approach for Architectural Information Modelling (AIM), which aims at describing historical information in construction and architecture directly related to design information and design practice. This paper will give an introduction into existing 3D modelling techniques and semantic description techniques, continuing with how these techniques are applied in the AIM approach. This investigation of 3D modelling and semantic technology shows promising results. However, in order to integrate these techniques into an AIM framework, more work is needed. Future work in this research project will therefore explore in further detail the semantic description scheme proposed below and the implementation of a proof-of-concept

A Bayesian Model for Exploiting Application Constraints to Enable Unsupervised Training of a P300-based BCI

This work introduces a novel classifier for a P300-based speller, which, contrary to common methods, can be trained entirely unsupervisedly using an Expectation Maximization approach, eliminating the need for costly dataset collection or tedious calibration sessions. We use publicly available datasets for validation of our method and show that our unsupervised classifier performs competitively with supervised state-of-the-art spellers. Finally, we demonstrate the added value of our method in different experimental settings which reflect realistic usage situations of increasing difficulty and which would be difficult or impossible to tackle with existing supervised or adaptive methods

IFC-based calculation of the Flemish Energy Performance Standard

This paper illustrates our findings concerning space based design methodologies and interoperability issues for today's Building Information Modeling (BIM) environments. A method is elaborated which enables building designers to perform an automated energy use analysis, based oil an Industry Foundation Classes (IFC) model derived from a commercial BIM environment, in this case Autodesk Revit 9.1. A prototype application was built, which evaluates the building model as well as vendor-neutral exchange mechanisms, in accordance with the Flemish Energy Performance Regulation (EPR) standard. Several issues regarding the need for space-based building models are identified and algorithms are developed to overcome possible shortcomings

Former land use affects the nitrogen and phosphorus concentrations and biomass of forest herbs

The colonization rates of understorey plants into forests growing on former agricultural land differ remarkably among species. Different dispersal and recruitment largely account for the contrasting colonization rates, but different effects of the soil legacies of former agricultural land use on plant performance may also play a role. Seven herbaceous forest species were sampled in paired post-agricultural and ancient forest stands to study whether land-use history has an effect on the aboveground nutrient concentrations (N, P and N:P ratios) and biomass of forest herbs and, if so, whether slow and fast colonizing species respond differently. Results showed that P concentrations were significantly affected by former land use with higher concentrations in the post-agricultural stands. N concentrations were unaffected and N:P ratios were significantly higher in the ancient stands. Nutrient concentrations varied considerably among species, but the variation was unrelated to their colonization capacity. Six out of the seven species had higher biomass in the post-agricultural stands relative to the ancient stands, and the degree to which the species increased biomass was positively related to their colonization capacity, i.e., the fast colonizing species showed the strongest increase. Such differential responses to past land use may contribute to the contrasting colonization capacity of forest plants. Land-use history thus affected both the nutrient concentrations and biomass of forest herbs, and only the biomass response was related to colonization capacity

Real-time epileptic seizure detection using reservoir computing

1 Purpose : This study proposes the use of a new classification algorithm, Reservoir Computing, to develop a real-time and accurate epileptic seizure detection system. 2 Methods: Reservoir Computing (RC) is a training method for recurrent neural networks where only a simple linear readout function is trained and where the neural network, the reservoir, is randomly created. As input for this reservoir we use a selection of different EEG features currently existing in seizure detection literature. This selection was made during training using a basic feature selection method. The output of the reservoir was trained using a ridge regression algorithm. 3 Results : In this study intracranial rat data from two different types of generalized epilepsy are detected: absence and tonic-clonic epilepsy. For both seizure types our approach resulted in an area under the Receiver Operating Characteristics curve (AUC) of 0.99 on the test data. For absences an average detection delay of 0.3s was noted, for tonic-clonic seizures this was 1.5s. The SWD detection method was tested on 15 hours of EEG-data coming from 13 GAERS rats, from which 10% was used for training. Our method outperformed the other implemented methods from which the best method was developed by Fanselow et al. in 2000 and resulted in an AUC of 0.96 and an average detection delay of more than 3 seconds. To evaluate the tonic-clonic seizure detection method 4 hours and 23 minutes of data of 4 rats was used. 20% of the total dataset was used for training, the rest was used for testing. Again our method outperformed other methods where the best method by White et al. in 2006 which resulted in a AUC of 0.82. 4 Conclusion : This study shows that it is possible to perform seizure detection using the described Reservoir Computing method and that it outperforms existing methods

Ontwikkeling van een Google SketchUp-plugin als ontwerpinstrument voor een energiezuinige architectuur

Sinds 1 januari 2006 is in Vlaanderen de energieprestatieregelgeving van kracht. Vlaamse architecten, ontwerpers en ingenieurs worstelen met deze nieuwe wetgeving en zijn nog steeds op zoek naar een instrument waarmee ze reeds van in de beginfase van het onwerp het peil van energieprestatie kunnen bepalen. Deze paper behandelt het onderzoek naar en de ontwikkeling van een Google SketchUp-plugin, dat kadert in de masterscriptie van Tine Jonckheere. Er werd onderzocht wat de specifieke noden zijn van de architecten in Vlaanderen en welke oplossingen er reeds voorhanden zijn. Vervolgens werd een prototype van de SketchUp-plugin ontwikkeld en getoetst aan de eisen en noden van de architect