Martin Spüler

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The goal of a Brain-Computer Interface (BCI) is to control a computer by pure brain activity. Recently, BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present a c-VEP BCI that uses online adaptation of the classifier to reduce calibration time and(More)
The goal of a Brain-Computer Interface (BCI) is to enable communication by pure brain activity without the need for muscle control. Recently BCIs based on code-modulated visual evoked potentials (c-VEPs) have shown great potential to establish high-performance communication. In this paper we present two new methods to improve classification in a c-VEP BCI.(More)
When a person recognizes an error during a task, an error-related potential (ErrP) can be measured as response. It has been shown that ErrPs can be automatically detected in tasks with time-discrete feedback, which is widely applied in the field of Brain-Computer Interfaces (BCIs) for error correction or adaptation. However, there are only a few studies(More)
Motor recovery after stroke is an unsolved challenge despite intensive rehabilitation training programs. Brain stimulation techniques have been explored in addition to traditional rehabilitation training to increase the excitability of the stimulated motor cortex. This modulation of cortical excitability augments the response to afferent input during motor(More)
OBJECTIVE To investigate whether error-related potentials can be used to increase information transfer rate of a P3 brain-computer interface (BCI) in healthy and motor-impaired individuals. METHODS Extraction and classification of the error-related potential was performed offline on data recorded from six amyotrophic lateral sclerosis (ALS) patients. An(More)
Classification of evoked or event-related potentials is an important prerequisite for many types of brain-computer interfaces (BCIs). To increase classification accuracy, spatial filters are used to improve the signal-to-noise ratio of the brain signals and thereby facilitate the detection and classification of evoked or event-related potentials. While(More)
One of the biggest problems in today’s BCI research is the non-stationarity of the recorded signals. This non-stationarity can cause the BCI performance to deteriorate over time or drop significantly when transferring data from one session to another. To reduce the effect of non-stationaries, we propose a new method for covariate shift adaption that is(More)
INTRODUCTION Different techniques for neurofeedback of voluntary brain activations are currently being explored for clinical application in brain disorders. One of the most frequently used approaches is the self-regulation of oscillatory signals recorded with electroencephalography (EEG). Many patients are, however, unable to achieve sufficient voluntary(More)
OBJECTIVE Recently, there have been several approaches to utilize a brain-computer interface (BCI) for rehabilitation with stroke patients or as an assistive device for the paralyzed. In this study we investigated whether up to seven different hand movement intentions can be decoded from epidural electrocorticography (ECoG) in chronic stroke patients. (More)
Brain-state-dependent stimulation (BSDS) combines brain-computer interfaces (BCIs) and cortical stimulation into one paradigm that allows the online decoding for example of movement intention from brain signals while simultaneously applying stimulation. If the BCI decoding is performed by spectral features, stimulation after-effects such as artefacts and(More)