Nataliya Kos'myna

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In this article, we introduce CLBCI (Co-Learning for Brain--Computer Interfaces), a BCI architecture based on co-learning in which users can give explicit feedback to the system rather than just receiving feedback. CLBCI is based on minimum distance classification with Independent Component Analysis (ICA) and allows for shorter training times compared to(More)
We present a Brain Computer Interface (BCI) system in an asynchronous setting that allows classifying objects in their semantic categories (e.g. a hammer is a tool). For training, we use visual cues that are representative of the concepts (e.g. a hammer image for the concept of hammer). We evaluate the system in an offline synchronous setting and in an(More)
Brain Computer Interface systems (BCIs) rely on lengthy training phases that can last up to months due to the inherent variability in brainwave activity between users. We propose a BCI architecture based on the co-learning between the user and the system through different feedback strategies. Thus, we achieve an operational BCI within minutes. We apply our(More)
Brain Computer Interface systems rely on lengthy training phases that can last up to months due to the inherent variability in brainwave activity between users. We propose a BCI architecture based on the co-learning between the user and the system through different feedback strategies. Thus, <i>we</i> achieve an operational BCI within minutes. We apply our(More)
Smart homes have been an active area of research, however despite considerable investment, they are not yet a reality for end-users. Moreover, there are still accessibility challenges for the elderly or the disabled, two of the main potential targets for home automation. In this exploratory study we design a control mechanism for smart homes based on Brain(More)
Using Brain Computer Interfaces (BCIs) as a control modality for games is popular. However BCIs require prior training before playing, which is hurtful to immersion and player experience in the game. For this reason, we propose an explicit integration of the training protocol in game by a modification of the game environment to enforce the synchronicity(More)
several different techniques, including magnetic resonance imaging (MRI), spectroscopy, and the most accessible method—electroencephalography (EEG)—which uses sensors to measure the electrical current produced by the brain. The ideal course of action to achieve the best signal quality would be to drill your skull open and put some electrodes directly on(More)
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