Clustering technique-based least square support vector machine for EEG signal classification
Computer Methods and Programs in Biomedicine
An algorithm for on-line detection of high frequency oscillations related to epilepsy
Computer Methods and Programs in Biomedicine
Design of virtual keyboard using blink control method for the severely disabled
Computer Methods and Programs in Biomedicine
BCI using imaginary movements: The simulator
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Automatic classification of sleep stages based on the time-frequency image of EEG signals
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Evaluating an online pharmaceutical education system for pharmacy interns in critical care settings
Computer Methods and Programs in Biomedicine
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In this paper, a concentration evaluation of reading behaviors with electrical signal detection on the head is presented. The electrode signal is extracted by brain-computer-interface (BCI) to monitor the user's degree of concentration, where the user is reminded by sound to concentrate, or teaching staffs are reminded to help users improve reading habits, in order to facilitate the user's ability to concentrate. The digital signal processing methods, such as the Kalman Filter, Fast Fourier Transform, the Hamming window, the average value of the total energy of a frame, correlation coefficient, and novel judgment algorithm are used to obtain the corresponding parameters of concentration evaluation. Users can correct their manner of reading with reminders. The repeated test results may be expected to lie with a probability of 95%. Such model training results in better learning effect.