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Many brain computer–interface (BCI) systems depend on imagined movement to control external devices. But how to extract the imagination feature and classify them to control systems is an important problem. To simplify the complexity of the classification, the power band and a small number of electrodes have been used, but there is still a loss in classification accuracy in the state-of-art approaches. The critical problem is the machine learning art that when the signal into source has property of non-stationary causing the estimation of the population parameter to change over time. In this paper, we analyzed the performance of feature extraction method using several spatial filter such as common average reference (CAR), Laplacian (LAP), common spatial pattern analysis (CSP) and no-spatial filter techniques and feature reduction method using principle component analysis (PCA) based 90% rule variance and leave-one-out correct classification accuracy selection method; where support vector machine is the classifier. The simulation with non-stationary data set from BCI competition III-Iva shows that CAR best performance CSP method in non-stationary data and PCA with leave-one-out CCA could maintain CCA performance and reduced the trading off between training and testing 13.96% compared to not using PCA and 0.46% compared PCA with 90% variance.

In this paper, we explored the new method for extracting feature from the electroencephalography (EEG) signal based on linear regression technique with the orthonormal polynomial bases. At first, EEG signals from electrodes around motor cortex were selected and were filtered in both spatial and temporal filter using band pass filter for alpha and beta rhymic band which considered related to the synchronization and desynchonization of firing neurons population during motor imagery task. Signal from epoch length 1s were fitted into linear regression with Legendre polynomials bases and extract the linear regression weight as final features. We compared our feature to the state of art feature, power band feature in binary classification using support vector machine (SVM) with 5-fold cross validations for comparing the classification accuracy. The result showed that our proposed method improved the classification accuracy 5.44% in average of all subject over power band features in individual subject study and 84.5% of classification accuracy with forward feature selection improvement.

An optimal feature selection and extraction procedure is an important task that significantly affects the success of brain activity analysis in brain-computer interface (BCI) research area. In this paper, a novel method for extracting the optimal feature from electroencephalogram (EEG) signal is proposed. At first, a student’s-t-statistic method is used to normalize and to minimize statistical error between EEG measurements. And, 2D time-frequency data set from the raw EEG signal was extracted using discrete wavelet transform (DWT) as a raw feature, standard deviations and mean of 2D time-frequency matrix were extracted as a optimal EEG feature vector along with other basis feature of sub-band signals. In the experiment, data set 1 of BCI competition IV are used and classification using SVM to prove strength of our new method.

Brain computer interface (BCI) system allows users to direct interact with the surrounded environment just a blink of their thought. In doing this, the most relevant informative from the electroencelography (EEG) signals need to be extracted from the electrodes of the scalp. Neurophysiology studies have proved that the power density from corresponding electrodes and frequency could identify imagination of the left or right hand movement. They also proved that these feature vary strongly from one subject to another. These spatial-time-frequency components are the keys to unlock the optimal features from the large space of these power density features. In this paper, we proposed the optimal feature extracting method from the basic power density of EEG signal. At first, the all EEG signal from the electrodes were filter using both spatial and temporal filters to enhance the signal to noise ratio of EEG. Then, the time-frequency features were extracted using short-time Fourier transform (STFT) and average power in sub-window band. Genetic algorithm was applied to search for the optimal features. In our simulation, we used the dataset from BCI competition III, IV and the data experimented in our laboratory. To ensure the improvement of our proposed feature extraction method, we applied the extracted feature into the support vector machine.

Subject dependent nature of electroencelography (EEG) signal elicits in the imagination task cause the drop of accuracy of the classifier in the brain-computer interface when the system apply in different subjects or crossing session experiment. The main components that have effect most in this problem are spatial-spectral-temporal parameter of the EEG signal that need to extract to find the optimal solution in the BCI system. In this paper we proposed a method for extracting the optimal parameters based on particle swarm optimization algorithm. First EEG signals were enhanced by Laplace and band pass filter. Optimal spatio-spectral-temporal component of Principle Component Analysis were search by Particle Swarm Optimization (PSO) using Short Time Fourier Transform features and classification error rate from Support Vector Machine (SVM) as fitness function. With optimal parameters, principle component from the STFT features were extracted and combined into single optimal feature vector. 5 fold-cross validations are applied to SVM.