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Recently, there are many studies on brain computer interface (BCI) system and some use EEG response at oddball paradigms. The aim of this study is to extract the features from the EEG signal speedy and safety. In this paper, we construct the state-space model of the EEG signal, and report the results to extract the features from the EEG signal included (or not included) VEP. It is confirmed that a...
Electroencephalograph (EEG) recordings during right and left hand motor imagery can be used to move a cursor to a target on a computer screen (such system is called BCI). Recently, we have proposed the detection method of error potential in order to add the fail safe function to BCI system. In this paper, feature extraction method based on morphological multi-resolution analysis is introduced to extract...
Recently, there are many studies on brain computer interface (BCI) system and some use EEG response at oddball paradigms. The aim of this paper is to extract feature (i.e. P300 response) from the EEG signals to improve the spelling system. We propose the method to analyze the averaged EEG signal concerned time and spatial. It is confirmed that the processing period with feature extraction is able...
In order to operate a robot, various devices are proposed such as a joystick, a master-slave arm, and keyboard. However, these devices have problems such as response speed. Instead of these devices, biosignal such as myoelectric signal is used as an interface between human and machine. The myoelectric signal represents the forces generated by muscle. In this paper, we propose to use the myoelectric...
The authors propose a new method for reducing both impulsive noise and white noise by use of M-transform and wavelet shrinkage. M-transform is a new signal transformation proposed by the authors, and any periodic time signal can be considered as the output of a filter whose input is an M-sequence. By using the properties of M-transform, it is shown that both impulsive noise and white noise can be...
In this paper, electroencephalograph (EEG) signals recorded during miss operation of brain computer interface (BCI) system was analyzed. As a result, a feature difference between miss operation and normal operation was found at the low frequency component and the event-related potential (ERP) such as P300. Therefore a possibility of the modification of miss operation was confirmed.
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