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In this paper, we address the problem of finding the best wavelet basis in wavelet packet analysis for applications based on classification. We implement and evaluate our proposed method in the design of a self-paced 2-state mental task-based brain-computer interface (BCI) as one possible type of classification-based applications. The autoregressive coefficients of the best wavelet basis are concatenated...
The stationary wavelet packet analysis is exploited for the first time in the design of a self-paced BCI based on mental tasks. The BCI system is custom designed to achieve a zero false positive rate, as false activations highly restricts the applications of BCIs in real life. The EEG signals of four subjects performing five different mental tasks are used as the dataset. The stationary wavelet packets...
To design a practical brain-computer interface, the high rate of false activation and the high number of necessary electrodes are two major problems that must be addressed. The objective of this study is to design a brain interface system that requires very few channels, has a zero false activation rate and a high true activation rate. To attain this objective, a brain-computer interface that is EEG-based...
Most brain-computer interface applications in real-life suffer from the high rate of false activations. The ultimate goal when designing brain-computer interfaces is to reach the zero false activation rate while the true activation rate is kept at a high level. In this study, a brain-computer interface design is shown to have a zero false activation rate. The interface is based on different mental...
At present, brain-computer interfaces cannot be used in real-life applications mainly because of their high false activation rates. To achieve a zero false positive rate, a mental task-based brain-computer interface custom designed for each subject and each task is proposed. The most discriminatory mental task is determined for each subject. We used the EEG signals of four subjects recorded while...
In this paper, we propose a method for online Farsi handwritten words recognition. At first, words are broken to their sub-words. Each sub-word is made of some strokes. We assign a tag to each sub-word based on the positions and shapes of its sub-strokes. After that, we classify sub-words according to their tags. Some online features are extracted from the main-stroke after the preprocessing stage...
Ear structure as a new class of biometrics can be used in many applications such as security systems. Ear structure is physiologically unique and stable, so ear recognition can be a good choice for a biometric security system. Even though, using ear biometric is not customary but it can be used with other biometrics like face or fingerprint simultaneously to increase the reliability of a biometric...
In this paper, we propose a method for online Farsi handwritten words recognition. At first, words are broken to their sub-words. Each sub-word is made of some strokes. The sign of the sub-word is found from the positions and shapes of its sub-strokes. After that, we classify sub-words according to their signs. Some online features are extracted from the main-stroke after the preprocessing stage....
Fractal theory has been used for computer graphics, image compression and different fields of pattern recognition. In this paper we simplified a general purpose two-dimensional fractal coder used for image compression. Since in the case of on-line signature recognition, we loose gray levels, contrast and luminosity information, we do not employ these parameters in the fractal coder. Instead, we focused...
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