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Comparison of performance of different feature extraction methods in detection of P300

Amini Z., Abootalebi V., Sadeghi M. T.

Biocybernetics and Biomedical Engineering

2013

Vol. 33, no. 1

3--20

The aim of this paper is to design a pattern recognition based system to detect the P300 component in the EEG trials. This system has two main blocks, feature extraction and classification. In the feature extraction block, in addition to morphological features, some new features including intelligent segmentation, common spatial pattern (CSP) and combined features (CSP + Segmentation) have also been used. Two criteria were used for the feature evaluation. Firstly, a t-test has been applied. Secondly, each of these four groups of features was evaluated by a Linear Discriminant Analysis (LDA) classifier. Afterwards, the best set of features was selected by using Stepwise Linear Discriminant Analysis (SWLDA). In the classification phase, the LDA was used as a linear classifier. The algorithm described here was tested with dataset II from the BCI competition 2005. In this research, the best result for the P300 detection was 97.4% .This result has proven to be more accurate than the results of previous works carried out in this filed.

Sterowanie prostym urządzeniem elektrycznym za pomocą sygnału EEG

Broniec A., Chodak J.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

2009

T. 13, z. 3/1

1059-1067

Szczegółowe poznanie, w jaki sposób pracuje mózg ludzki, otwiera szerokie możliwości w konstrukcji interfejsów bezpośredniej komunikacji pomiędzy człowiekiem a komputerem (brain-computer interface - BCI). Badania nad interfejsami mózg-komputer to rozwijająca się dynamicznie od około 15 lat tematyka, powstała na pograniczu wielu dziedzin naukowych. Celem było stworzenie prostego interfejsu BCI, opartego na wykrywaniu fali alfa, który umożliwi użytkownikowi sterowanie (włączanie/wyłączanie) urządzeniem elektrycznym. W tym celu wykorzystano kolejkę elektryczną, poruszającą się jedynie w przypadku wykrycia fali alfa w sygnale EEG.

Detailed knowledge of changes in electroencephalography signal (EEG) which occur during various human activities gives enormous possibilities to create brain-computer interfaces (BCI). The BCI research is progressively expanding and extremely important field in biomedical engineering. These brain-computer interfaces give hope to people with severe disabilities for improving their quality of life. There is also a wide range of BCI applications in the field of virtual reality and mind-controlled games. The aim of our project was to create a simple game using a railway toy, which is able to move by conscious changes in brain activity of players. The EEG signal is acquired using a set of electrodes placed on the heads of the players and it is processed in real time by dedicated algorithms. This project has been performed for the educational and cognitive purposes.