Learning from EEG Error-related Potentials in Noninvasive Brain-Computer Interfaces
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EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable variations in the signal due to artefacts or recogniser-subject feedback. A number of techniques were recently develope ...
Non-invasive brain-computer interfaces are traditionally based on mu rhythms, beta rhythms, slow cortical potentials or P300 event-related potentials. However, there is mounting evidence that neural oscillations up to 200 Hz play important roles in process ...
People with severe motor disabilities (spinal cord injury (SCI), amyotrophic lateral sclerosis (ALS), etc.) but with intact brain functions are somehow prisoners of their own body. They need alternative ways of communication and control to interact with th ...
EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable variations in the signal due to artefacts or recogniser-subject feedback. A number of techniques were recently develope ...
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Brain-computer interfaces, as any other interaction modality based on physiological signals and body channels (e.g., muscular activity, speech and gestures), are prone to errors in the recognition of subject's intent. In this paper we exploit a unique feat ...
After the recent development of the theory of nonlinear dynamical systems and deterministic chaos and the introduction of one, in theory, simple method for computing the phase space, many researchers started to analyze electroencephalographic (EEG) signals ...
People with severe motor disabilities (spinal cord injury (SCI), amyotrophic lateral sclerosis (ALS), etc.) but with intact brain functions are somehow prisoners of their own body. They need alternative ways of communication and control to interact with th ...
Spatial filtering is a widely used dimension reduction method in electroencephalogram based brain-computer interface systems. In this paper a new algorithm is proposed, which learns spatial filters from a training dataset. In contrast to existing approache ...
