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Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output(More)
Interest in developing a new method of man-to-machine communication--a brain-computer interface (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(More)
Designing a brain computer interface (BCI) system one can choose from a variety of features that may be useful for classifying brain activity during a mental task. For the special case of classifying electroencephalogram (EEG) signals we propose the usage of the state of the art feature selection algorithms Recursive Feature Elimination and Zero-Norm(More)
During the last ten years there has been growing interest in the development of Brain Computer Interfaces (BCIs). The field has mainly been driven by the needs of completely paralyzed patients to communicate. With a few exceptions, most human BCIs are based on extracranial elec-troencephalography (EEG). However, reported bit rates are still low. One reason(More)
We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five(More)
The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG), for the use in a brain computer interface (BCI). This is especially helpful for evaluating quickly whether a BCI approach based on electroencephalography, on which training may(More)
This study investigates the neurophysiological basis of EEG feedback for patients with epilepsy. Brain areas are identified that become hemodynamically deactivated when epilepsy patients, trained in EEG self-regulation, generate positive slow cortical potentials (SCPs). Five patients were trained in producing positive SCPs, using a training protocol(More)
The thought-translation device (TTD) consists of a training device and spelling program for the completely paralyzed using slow-cortical brain potentials (SCP). During the training phase, the self-regulation of SCPs is learned through visual-auditory feedback and positive reinforcement of SCPs; during the spelling phase, patients select letters or words(More)
OBJECTIVE The Thought Translation Device (TTD) for brain-computer interaction was developed to enable totally paralyzed patients to communicate. Patients learn to regulate slow cortical potentials (SCPs) voluntarily with feedback training to select letters. This study reports the comparison of different methods of electroencephalographic (EEG) analysis to(More)
OBJECTIVE We investigated the effects of self-regulation of slow cortical potentials for children with attention-deficit/hyperactivity disorder. Slow cortical potentials are slow event-related direct-current shifts of the electroencephalogram. Slow cortical potential shifts in the electrical negative direction reflect the depolarization of large cortical(More)