Pharino Chum

Optimal EEG feature selection by genetic algorithm for classification of imagination of hand movement

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.

Orthonormal Polynomial based Optimal EEG Feature Extraction for Motor Imagery Brain-Computer Interface

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.

Parallel Model Feature Extraction to Improve Performance of a BCI System

It is well knowns that based on the CSP (Common Spatial Pattern) algorithm, the linear projection of an EEG (Electroencephalography) signal can be made to spaces that optimize the discriminant between two patterns. Sharing disadvantages from linear time invariant systems, CSP suffers from the non-stationary nature of EEGs causing the performance of the classification in a BCI (Brain-Computer Interface) system to drop significantly when comparing the training data and test data. The author has suggested a simple idea based on the parallel model of CSP filters to improve the performance of BCI systems. The model was tested with a simple CSP algorithm (without any elaborate regularizing methods) and a perceptron learning algorithm as a classifier to determine the improvement of the system. The simulation showed that the parallel model could improve classification performance by over 10% compared to conventional CSP methods.

Particle Swarm Optimization Based Optimal Spatial-Spectral-Temporal Component Search in Motor Imagery Brain-Computer Interface

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.

Optimal EEG Feature Extraction using DWT for Classification of Imagination of Hands Movement

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.