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Brain-Computer Interface (BCI) research has become a growing field of interest in the last years. The work presented ranges from machine learning approaches in offline results to the application of a BCI in patients. However, reliable classification of brain activity is a crucial issue in BCI research. In contrast to most articles which present methods to(More)
Nowadays, everybody knows what a hybrid car is. A hybrid car normally has two engines to enhance energy efficiency and reduce CO2 output. Similarly, a hybrid brain-computer interface (BCI) is composed of two BCIs, or at least one BCI and another system. A hybrid BCI, like any BCI, must fulfill the following four criteria: (i) the device must rely on signals(More)
Software development is a key issue in brain-computer interface (BCI) research. Software can show the similarities and differences of different data processing methods. It can also make clear which hyperparameters must be determined for particular algorithms. And it can demonstrate whether certain concepts are compatible or not. With BioSig's comprehensive(More)
An adaptive P300 brain-computer interface (BCI) using a 12 × 7 matrix explored new paradigms to improve bit rate and accuracy. During online use, the system adaptively selects the number of flashes to average. Five different flash patterns were tested. The 19-flash paradigm represents the typical row/column presentation (i.e. 12 columns and 7 rows). The 9-(More)
Currently, almost all brain-computer interfaces (BCIs) ignore the relationship between phases of electroencephalographic signals detected from different recording sites (i.e., electrodes). The vast majority of BCI systems rely on feature vectors derived from e.g., bandpower or univariate adaptive autoregressive (AAR) parameters. However, ample evidence(More)
Advances in cognitive neurosci- ence and brain-imaging technologies give us the unprecedented ability to interface directly with brain activity. These technologies let us monitor the physical processes in the brain that correspond with certain forms of thought. Driven by society's growing recognition of the needs of people with physical disabilities,(More)
The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include(More)
OBJECTIVE Many brain-computer interfaces (BCIs) use band power (BP) changes in the electroencephalogram to distinguish between different motor imagery (MI) patterns. Most current approaches do not take connectivity of separated brain areas into account. Our objective is to introduce single-trial connectivity features and apply these features to BCI data. (More)
This document provides a review of the techniques and therapies used in gait rehabilitation after stroke. It also examines the possible benefits of including assistive robotic devices and brain-computer interfaces in this field, according to a top-down approach, in which rehabilitation is driven by neural plasticity.The methods reviewed comprise classical(More)