A study on temporal segmentation strategies for extracting common spatial patterns for brain computer interfacing

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

Wavelet Lifting over Information-Based EEG Graphs for Motor Imagery Data Classification

The imagination of limb movements offers an intuitive paradigm for the control of electronic devices via brain computer interfacing (BCI). The analysis of electroencephalographic (EEG) data related to motor imagery potentials has proved to be a difficult task. EEG readings are noisy, and the elicited patterns occur in different parts of the scalp, at different instants and at different frequencies. Wavelet transform has been widely used in the BCI field as it offers temporal and spectral capabilities, although it lacks spatial information. In this study we propose a tailored second generation wavelet to extract features from these three domains. This transform is applied over a graph representation of motor imaginary trials, which encodes temporal and spatial information. This graph is enhanced using per-subject knowledge in order to optimise the spatial relationships among the electrodes, and to improve the filter design. This method improves the performance of classifying different imaginary limb movements maintaining the low computational resources required by the lifting transform over graphs. By using an online dataset we were able to positively assess the feasibility of using the novel method in an online BCI context

Wavelet design by means of multi-objective GAs for motor imagery EEG analysis

Wavelet-based analysis has been broadly used in the study of brain-computer interfaces (BCI), but in most cases these wavelet functions have not been designed taking into account the requirements of this field. In this study we propose a method to automatically generate wavelet-like functions by means of genetic algorithms. Results strongly indicate that it is possible to generate (evolve) wavelet functions that improve the classification accuracy compared to other well-known wavelets (e.g. Daubechies and Coiflets)

Multiresolution analysis over graphs for a motor imagery based online BCI game

Multiresolution analysis (MRA) over graph representation of EEG data has proved to be a promising method for offline brain–computer interfacing (BCI) data analysis. For the first time we aim to prove the feasibility of the graph lifting transform in an online BCI system. Instead of developing a pointer device or a wheel-chair controller as test bed for human–machine interaction, we have designed and developed an engaging game which can be controlled by means of imaginary limb movements. Some modifications to the existing MRA analysis over graphs for BCI have also been proposed, such as the use of common spatial patterns for feature extraction at the different levels of decomposition, and sequential floating forward search as a best basis selection technique. In the online game experiment we obtained for three classes an average classification rate of 63.0% for fourteen naive subjects. The application of a best basis selection method helps significantly decrease the computing resources needed. The present study allows us to further understand and assess the benefits of the use of tailored wavelet analysis for processing motor imagery data and contributes to the further development of BCI for gaming purposes

Translation from Braille Music Mark-up Language to DAISYXML

As result of the Contrapunctus European project the design of the Braille Music Mark-up as an XML representation of a music scores in Braille has been carried out. We propose a design of a prototype system for translating these kinds of files into spoken music encoded in DAISYXML. In this way any blind musician may be able to memorize any Braille score using a DAISY reader. Therefore the dependency of reading BMML files in front of a computer would be eliminated. This is a first work on feasibility which will be improved and managed by a working group

Extracting optimal tempo-spatial features using local discriminant bases and common spatial patterns for brain computer interfacing

Brain computer interfaces (BCI) provide a new approach to human computer communication, where the control is realised via performing mental tasks such as motor imagery (MI). In this study, we investigate a novel method to automatically segment electroencephalographic (EEG) data within a trial and extract features accordingly in order to improve the performance of MI data classification techniques. A new local discriminant bases (LDB) algorithm using common spatial patterns (CSP) projection as transform function is proposed for automatic trial segmentation. CSP is also used for feature extraction following trial segmentation. This new technique also allows to obtain a more accurate picture of the most relevant temporal–spatial points in the EEG during the MI. The results are compared with other standard temporal segmentation techniques such as sliding window and LDB based on the local cosine transform (LCT)

Using virtual learning environments in bricolage mode for orchestrating learning situations across physical and virtual spaces

Producción CientíficaTeachers usually implement their pedagogical ideas in Virtual Learning Environments (VLEs) in a continuous refinement approach also known as “bricolage”. Recently, different proposals have enabled the ubiquitous access to VLEs, thus extending the bricolage mode of operation to other learning spaces. However, such proposals tend to present several limitations for teachers to orchestrate learning situations conducted across different physical and virtual spaces. This paper presents an evaluation study that involved the across-spaces usage of Moodle in bricolage mode and learning buckets (configurable containers of learning artifacts) in multiple learning situations spanning five months in a course on Physical Education in the Natural Environment for pre-service teachers. The study followed a responsive evaluation model, in which we conducted an anticipatory data reduction using an existing orchestration framework (called “5 + 3 aspects”) for structuring data gathering and analysis. The results showed that learning buckets helped the teachers in the multiple aspects of orchestration, overcoming the limitations of alternative approaches in some specific orchestration aspects: helping the involved teachers to connect different physical and physical spaces, while supporting technologies and activities of their everyday practice, and transferring part of the orchestration load from teachers to students. The results also suggested lines of future improvement, including the awareness of outdoor activities.Ministerio de Economía, Industria y Competitividad (Project TIN2011-28308-C03-02 and TIN2014-53199-C3-2-R)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA277U14 and VA082U16

Game of Blazons: Helping teachers conduct learning situations that integrate web tools and multiple types of augmented reality

Producción CientíficaSeveral studies have explored how to help teachers carry out learning situations involving Augmented Reality (AR), a technology that has shown different affordances for learning. However, these proposals tend to rely on specific types of AR, focus on particular types of spaces, and are generally disconnected from other technologies widely used in education, such as VLEs or Web 2.0 tools. These constraints limit the possible range of activities that can be conducted and their integration into the existing classroom practice. GLUEPS-AR is a system that can help overcome these limitations, aiding teachers in the creation and enactment of learning situations that may combine multiple types of AR with other common web tools. This paper presents an evaluation study conducted on Game of Blazons, a learning situation carried out by two university teachers using GLUEPS-AR, and framed within two days of outdoor activities in a village in Spain. The evaluation showed that GLUEPS-AR provided an affordable support to the participant teachers to integrate several activities that made use of multiple types of AR, common web tools and augmented paper, into a unique learning situation.Ministerio de Economía, Industria y Competitividad (Projects TIN2011-28308-C03-02 and TIN2014-53199-C3-2-R)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA082U16

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection

Background: Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. Methods: This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. Results and conclusion: The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed

Monitoring for awareness and reflection in ubiquitous learning environments

Producción CientíficaDespite the educational affordances that ubiquitous learning has shown, it is still hampered by several orchestration difficulties. One of these difficulties is that teachers lose awareness of what the students perform across the multiple technologies and spaces involved. Monitoring can help in such awareness, and it has been highly explored in face-to-face and blended learning. Nevertheless, in ubiquitous learning environments monitoring has been usually limited to activities taking place in a specific type of space (e.g., outdoors). In this paper we propose a monitoring system for ubiquitous learning, which was evaluated in three authentic studies, supporting the participants in the affordable monitoring of learning situations involving web, augmented-physical, and 3D virtual world spaces. The work carried out also helped identify a set of guidelines, which are expected to be useful for researchers and technology developers aiming to provide participants’ support in ubiquitous learning environments