2,406 research outputs found

    A high-performance speech neuroprosthesis

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    Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speech into tex

    Slow Sphering to Suppress Non-Stationaries in the EEG

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    Non-stationary signals are ubiquitous in electroencephalogram (EEG) signals and pose a problem for robust application of brain-computer interfaces (BCIs). These non-stationarities can be caused by changes in neural background activity. We present a dynamic spatial filter based on time local whitening that significantly reduces the detrimental influence of covariance changes during event-related desynchronization classification of an imaginary movement task

    Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces

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    We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of non-invasive BCIs

    Advancing Pattern Recognition Techniques for Brain-Computer Interfaces: Optimizing Discriminability, Compactness, and Robustness

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    In dieser Dissertation formulieren wir drei zentrale Zielkriterien zur systematischen Weiterentwicklung der Mustererkennung moderner Brain-Computer Interfaces (BCIs). Darauf aufbauend wird ein Rahmenwerk zur Mustererkennung von BCIs entwickelt, das die drei Zielkriterien durch einen neuen Optimierungsalgorithmus vereint. DarĂŒber hinaus zeigen wir die erfolgreiche Umsetzung unseres Ansatzes fĂŒr zwei innovative BCI Paradigmen, fĂŒr die es bisher keine etablierte Mustererkennungsmethodik gibt

    Towards improved visual stimulus discrimination in an SSVEP BCI

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    The dissertation investigated the influence of stimulus characteristics, electroencephalographic (EEG) electrode location and three signal processing methods on the spectral signal to noise ratio (SNR) of Steady State Visual Evoked Potentials (SSVEPs) with a view for use in Brain-Computer Interfaces (BCIs). It was hypothesised that the new spectral baseline processing method introduced here, termed the 'activity baseline', would result in an improved SNR

    Engagement and arousal effects in predicting the increase of cognitive functioning following a neuromodulation program

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    Research in the field of Brain-Computer Interfaces (BCIs) has increased exponentially over the past few years, demonstrating their effectiveness and application in several areas. The main purpose of the present paper was to explore the relevance of user engagement during interaction with a BCI prototype (Neuro-Upper, NU), which aimed at brainwave synchronization through audio-visual entrainment, in the improvement of cognitive performance

    Fatigue evaluation through EEG analysis using multi-scale entropy in SSVEP-based BCIs

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    INTRODUCTION: Fatigue is a big challenge when moving a steady state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) from laboratory into real-life applications [1], as it not only harms the system performance, but also causes users’ discomfort. Towards eventually fatigue reduction, an accurate and objective evaluation of fatigue level is the first and also a crucial step. On the other hand, multi-scale entropy (MSE) can ...published_or_final_versio

    Decoding Complex Imagery Hand Gestures

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    Brain computer interfaces (BCIs) offer individuals suffering from major disabilities an alternative method to interact with their environment. Sensorimotor rhythm (SMRs) based BCIs can successfully perform control tasks; however, the traditional SMR paradigms intuitively disconnect the control and real task, making them non-ideal for complex control scenarios. In this study, we design a new, intuitively connected motor imagery (MI) paradigm using hierarchical common spatial patterns (HCSP) and context information to effectively predict intended hand grasps from electroencephalogram (EEG) data. Experiments with 5 participants yielded an aggregate classification accuracy--intended grasp prediction probability--of 64.5\% for 8 different hand gestures, more than 5 times the chance level.Comment: This work has been submitted to EMBC 201
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