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This paper presents a comparison of Electroencephalogram (EEG) signals classification for Brain Computer-Interfaces (BCI). At present, it is a challenging task to extract the meaningful EEG signal patterns from a large volume of poor quality data and simultaneously with the presence of artifacts noises. Selection of the effective classification technique of the EEG signals at classification stage...
A biometric person authentication system using brain waves or Electroencephalogram (EEG) signals recorded using a minimum number of channels ranging from 2 to 6 is presented. The task for EEG recording consists of simple motor imagery movements that the subject has to imagine. The system uses an effective time-frequency based feature extraction method using the short-time Fourier transform (STFT)...
In this paper, the support vector machines (SVMs) is adopted for distinguishing between normal and epileptic EEG time series. The embedding dimension of electroencephalogram (EEG) time series is used as the input feature for detecting epileptic seizure automatically. Cao's method is applied for computing the embedding dimension of normal and epileptic EEG time series. In the last work, probabilistic...
Brain-computer interface (BCI) system uses brain activity to control external devices such as computers and electronic devices. It is a novel kind of human computer interaction. BCI system can be regard as pattern recognition system, and the key point is classification of Electroencephalogram (EEG) signals under different mental tasks. Classification algorithms of BCI system include Fisher linear...
The alcoholism is one of psychiatric phenotype, which results from interplay between genetic and environmental factors. Not only it leads to brain defects but also associated cognitive, emotional, and behavioral impairments. It can be detected by analyzing EEG signals. In this research, the power spectrum of the Haar mother wavelet is extracted as features. Then the principle component analysis is...
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