Zahra Amini; Vahid Abootalebi; Mohammad Taghi Sadeghi
Volume 4, Issue 4 , June 2010, , Pages 293-306
Abstract
The aim of this paper is to design a pattern recognition based system to detect P300 component in multi-channel electroencephalogram (EEG) trials. This system has two main blocks, feature extraction and classification. In feature extraction block, in addition to conventional features namely morphological, ...
Read More
The aim of this paper is to design a pattern recognition based system to detect P300 component in multi-channel electroencephalogram (EEG) trials. This system has two main blocks, feature extraction and classification. In feature extraction block, in addition to conventional features namely morphological, frequency and wavelet features, some new features included intelligent segmentation, common spatial pattern (CSP) and combined features (CSP + Segmentation) have also been used. Three criteria were used for evaluation and selection of a feature set by choosing a subset of the original features that contains most of essential information. Firstly, a statistical analysis has been applied for evaluating the fitness of each feature in discriminating between target and non target signals. Secondly, each of these six groups of features was evaluated by a Linear Discriminant Analysis (LDA) classifier. Furthermore by using Stepwise Linear Discriminant Analysis (SWLDA), the best set of features was selected. Among these six feature vectors, intelligent segmentation was seen to be most efficient in classification of these signals. In classification phase, two linear classifiers -LDA and SWLDA- were used. The algorithm was described here has tested with dataset II from the BCI competition 2005. In this research, the best result for P300 detection is 97.05% .This result have proven to be more accurate than the results of previous works carried out in this filed.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Vahid Abootalebi; Mohammad Hasan Moradi; Mohammad Ali Khalilzadeh
Volume -1, Issue 1 , June 2004, , Pages 25-45
Abstract
P300 is the most predominant cognitive component of the brain signals. In this study, the single trial event related potentials recorded from the scalp, were decomposed to their time-frequency components using discrete wavelet transform. These quantities were later analyzed as the features related to ...
Read More
P300 is the most predominant cognitive component of the brain signals. In this study, the single trial event related potentials recorded from the scalp, were decomposed to their time-frequency components using discrete wavelet transform. These quantities were later analyzed as the features related to the cognitive activities of brain. Study on these features showed that cognitive processes of the brain of ten reflected in the feature of δ and θ bands. The aim of this study, as a primary step for "lie detection using brain signals (EEG - Polygraphy)", was to design a system for discriminating between single trials involved P300 and those without it. In the first approach, an optimal discriminant function based on 9 features was designed using "Stepwise Linear Discriminant Analysis". Detection accuracy was 75% in training data and 71% in test data. More study on this method showed that almost similar accuracy could be obtained from the features of Pz channel alone. In the second approach, the modular learning strategy - based on principal component analysis and neural networks - was used. After training the systems, the maximum classification accuracy was 76% in train data and 72% in test data.
