Design and Implementation of an Online Brain Computer Interface System
DOI:
https://doi.org/10.14738/tmlai.v1i1.30Abstract
Brain Computer Interface is the communication channel between the brain and the computer for recording of electrical activity along the scalp produced by the firing of neurons within the brain. The brain signals which are also known as Electroencephalography (EEG) can be used to direct and control some external activity. This work reports a methodology for acquisition and detection and of EEG signals, and extraction of useful information in order to differentiate the signals related to particular type of movement. A modified Common Spatial Pattern (CSP) algorithm has been used at preprocessing stage. Logarithmic transform along with the information theoretic feature extraction has also been used for feature extraction. KNN, SVM and Artificial Neural Networks are employed for classification. The proposed methodology is tested on publically available data sets and the results are found to be comparable with the published approaches.
References
G. Pfurtscheller, “Graphical display and statistical evaluation of event related desynchronization (ERD),” Electroenc. Clin. Neurophys., vol. 43, pp. 757–760, 1977.
S. C. Gandevia and J. C. Rothwell, “Knowledge of motor commands and the recruitment of human motor neurons,” Brain, vol. 110, no. 5, pp. 1117–1130, 1987.
G. Pfurtscheller, C. Neuper, D. Flotzinger, and M. Pregenzer, “EEG based discrimination between imagination of right and left hand movement,” Electroenc. Clin. Neurophys., vol. 103, no. 5, pp. 1–10, 1997.
H. Ramoser, J. Mueller-Gerking, and G. Pfurtscheller, Optimal spatial filtering of single trial EEG during imagined hand movement, IEEE Trans. Rehabil. Eng., vol. 8, no. 4, pp. 441–446, Dec. 2000.
Cedric Gouy-Pailler, Jeremie Mattout, BCI Competition IV, Dataset 1: Motor Imagery, uncued classifier Application, August 2008, France.
Benjamin Blankertz, Klaus-Robert Muller, The BCI Competition III: Validating alternative Approaches to Actual BCI Problems, Neural and Rehabilitation Engineering, 2006
Hyohyeng Kang, Yunjun Nam, Seungjin Choi, Composite Common Spatial Pattern for subject to subject transfer, IEEE Signal Processing letters, Vol. 16, No-8, pages. 683-686, August 2009
G. Pfurtscheller, C. Neuper, D. Flotzinger, and M. Pregenzer, “EEG based discrimination between imagination of right and left hand movement,” Electroenc. Clin. Neurophys., vol. 103, no. 5, pp. 1–10, 1997.
Moritz Grosse-Wentrup, Martin Buss, Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction, IEEE transactions on Biomedical Engineering, Vol. 55, page. 1991-1999, August 2008
Artificial Neural networks and digital signal processing, danikos, 2007-2010: http://www.learnartificialneuralnetworks.com/
C.Brunner, R. Leeb, G.R. Muller-Putz, A. Schlogl, and G. Pfurtscheller, BCI competition 2008, Graz dataset A, Institute of Knowledge Discovery, Graz University of Technology, Austria, and Institute for Human-Computer Interfaces, Graz University of Technology, Austria.
Benajmin Blankertz, Klaus Robert Muller, Berlin BCI Group, Datasets-1V(a) BCI IV Description, (motor Imagery, uncued classifier Application), 2008
