Gerwin Schalk | Wadsworth Center (original) (raw)
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Papers by Gerwin Schalk
Journal of Neuroscience, 2008
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006
Brain Research Bulletin, 2008
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006
Journal of Neural Engineering, 2008
Journal of Neural Engineering, 2007
Journal of Neural Engineering, 2008
Journal of Neural Engineering, 2004
IEEE Transactions on Biomedical Engineering, 2004
Journal of Neuroscience, 2007
IEEE Transactions on Biomedical Engineering, 2004
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006
Abstract, Interest in developing a new method of man-to-machine communication, a brain-computer i... more Abstract, Interest in developing a new method of man-to-machine communication, a brain-computer interface or BCI, has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the
Abstract— A Brain-Computer Interface (BCI) is a system that allows its users to control external ... more Abstract— A Brain-Computer Interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands,is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user’s brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. This article describes the data sets that were provided to the competitors and gives an overview of the results. In a series of accompanying articles, the winning teams describe their methods in detail. Index Terms— augmentative communication, beta-rhythm,
Journal of Neuroscience, 2008
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006
Brain Research Bulletin, 2008
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006
Journal of Neural Engineering, 2008
Journal of Neural Engineering, 2007
Journal of Neural Engineering, 2008
Journal of Neural Engineering, 2004
IEEE Transactions on Biomedical Engineering, 2004
Journal of Neuroscience, 2007
IEEE Transactions on Biomedical Engineering, 2004
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006
Abstract, Interest in developing a new method of man-to-machine communication, a brain-computer i... more Abstract, Interest in developing a new method of man-to-machine communication, a brain-computer interface or BCI, has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the
Abstract— A Brain-Computer Interface (BCI) is a system that allows its users to control external ... more Abstract— A Brain-Computer Interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands,is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user’s brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. This article describes the data sets that were provided to the competitors and gives an overview of the results. In a series of accompanying articles, the winning teams describe their methods in detail. Index Terms— augmentative communication, beta-rhythm,