Adaptive multi-degree of freedom Brain Computer Interface using online feedback: Towards novel methods and metrics of mutual adaptation between humans and machines for BCI.

This paper proposes a novel adaptive online-feedback methodology for Brain Computer Interfaces (BCI). The method uses ElectroEncephaloGraphic (EEG) signals and combines motor with speech imagery to allow for tasks that involve multiple degrees of freedom (DoF). The main approach utilizes the covariance matrix descriptor as feature, and the Relevance Vector Machines (RVM) classifier. The novel contributions include, (1) a new method to select representative data to update the RVM model, and (2) an online classifier which is an adaptively-weighted mixture of RVM models to account for the users' exploration and exploitation processes during the learning phase. Instead of evaluating the subjects' performance solely based on the conventional metric of accuracy, we analyze their skill's improvement based on 3 other criteria, namely the confusion matrix's quality, the separability of the data, and their instability. After collecting calibration data for 8 minutes in the first run, 8 participants were able to control the system while receiving visual feedback in the subsequent runs. We observed significant improvement in all subjects, including two of them who fell into the BCI illiteracy category. Our proposed BCI system complements the existing approaches in several aspects. First, the co-adaptation paradigm not only adapts the classifiers, but also allows the users to actively discover their own way to use the BCI through their exploration and exploitation processes. Furthermore, the auto-calibrating system can be used immediately with a minimal calibration time. Finally, this is the first work to combine motor and speech imagery in an online feedback experiment to provide multiple DoF for BCI control applications.

Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features.

OBJECTIVE: In this paper, we investigate the suitability of imagined speech for brain-computer interface (BCI) applications. APPROACH: A novel method based on covariance matrix descriptors, which lie in Riemannian manifold, and the relevance vector machines classifier is proposed. The method is applied on electroencephalographic (EEG) signals and tested in multiple subjects. MAIN RESULTS: The method is shown to outperform other approaches in the field with respect to accuracy and robustness. The algorithm is validated on various categories of speech, such as imagined pronunciation of vowels, short words and long words. The classification accuracy of our methodology is in all cases significantly above chance level, reaching a maximum of 70% for cases where we classify three words and 95% for cases of two words. SIGNIFICANCE: The results reveal certain aspects that may affect the success of speech imagery classification from EEG signals, such as sound, meaning and word complexity. This can potentially extend the capability of utilizing speech imagery in future BCI applications. The dataset of speech imagery collected from total 15 subjects is also published.

Novel Views of Objects from a Single Image.

Taking an image of an object is at its core a lossy process. The rich information about the three-dimensional structure of the world is flattened to an image plane and decisions such as viewpoint and camera parameters are final and not easily revertible. As a consequence, possibilities of changing viewpoint are limited. Given a single image depicting an object, novel-view synthesis is the task of generating new images that render the object from a different viewpoint than the one given. The main difficulty is to synthesize the parts that are disoccluded; disocclusion occurs when parts of an object are hidden by the object itself under a specific viewpoint. In this work, we show how to improve novel-view synthesis by making use of the correlations observed in 3D models and applying them to new image instances. We propose a technique to use the structural information extracted from a 3D model that matches the image object in terms of viewpoint and shape. For the latter part, we propose an efficient 2D-to-3D alignment method that associates precisely the image appearance with the 3D model geometry with minimal user interaction. Our technique is able to simulate plausible viewpoint changes for a variety of object classes within seconds. Additionally, we show that our synthesized images can be used as additional training data that improves the performance of standard object detectors.

Conformational rearrangements in the S6 domain and C-linker during gating in CNGA1 channels.

This work completes previous findings and, using cysteine scanning mutagenesis (CSM) and biochemical methods, provides detailed analysis of conformational changes of the S6 domain and C-linker during gating of CNGA1 channels. Specific residues between Phe375 and Val424 were mutated to a cysteine in the CNGA1 and CNGA1(cys-free) background and the effect of intracellular Cd(2+) or cross-linkers of different length in the open and closed state was studied. In the closed state, Cd(2+) ions inhibited mutant channels A406C and Q409C and the longer cross-linker reagent M-4-M inhibited mutant channels A406C(cys-free) and Q409C(cys-free). Cd(2+) ions inhibited mutant channels D413C and Y418C in the open state, both constructed in a CNGA1 and CNGA1(cys-free) background. Our results suggest that, in the closed state, residues from Phe375 to approximately Ala406 form a helical bundle with a three-dimensional (3D) structure similar to those of the KcsA; furthermore, in the open state, residues from Ser399 to Gln409 in homologous subunits move far apart, as expected from the gating in K(+) channels; in contrast, residues from Asp413 to Tyr418 in homologous subunits become closer in the open state.