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Adopting the "Imitating-natural-reading(INR)" elicited N2_P3 as carriers between brain and computer. The feature selection and classification of the evoked potentials with wavelet transform and Back Propagation(BP) neural network was used to obtain brain-computer interface(BCI) control signal. Followed by using 20 single-channel electroen-cephalogram(EEG) to single-trial estimation, the...
The mind speller is a brain-computer interface which enables subjects to spell text on a computer screen by detecting P300 event-related potentials in their electroencephalograms. This BCI application is of particular interest for disabled patients who have lost all means of verbal and motor communication. We report on the implementation of a feature extraction procedure on a new ultra low-power 8-channel...
This paper reports preliminary results of steady-state movement related potential (ssMRP) classification using hidden Markov models (HMM). Published works on electroencephalogram (EEG) signal classification mainly need experimenter interventions to accurately define temporal boundaries between the resting and motor execution states for the classifier where for asynchronous brain computer interfacing...
This paper proposes a computer mouse-like device that is controlled by eye movements and voluntary eye winks using a single channel of EOG. The position of a pair electrode, and time-domain signal analysis are also presented.
Partially or completely paralyzed patients can benefit from Brain Computer Interface (BCI) in which a continuous recording of electroencephalogram (EEG) is required, operating some processing and classification to control a computer or other devices. Patients are so allowed to control external devices or to communicate simple messages through the computer, just concentrating their attention on codified...
Brain Computer Interface (BCI) is an alternative communication pathway between the human brain and outside world in which only the brain activity is interpreted in a special way. These systems are based on the electrical activity of the brain that can be measured via Electroencephalography (EEG) devices. BCI enables people with severe motor disorders (like ALS) to communicate with their environment...
A novel brain computer interface (BCI) is implemented in this study, which only depends on auditory modality. The subjects voluntary recognition of the property (e.g. voice laterality) of a target human voice makes the discriminability between brain responses to target and non-target voices in a random sequence. EEG data from eight subjects showed that the amplitude of N2 and late positive component...
In this paper, we present the results of single trial EEG classification of observed wrist movements. This study is part of our endeavour to develop brain computer interfaces as an assistive device for people with severe motor disabilities. Our methods rely on a simple but robust algorithm that requires no subject training to modulate brain activity. We adopt a method based on extraction and selection...
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