In this paper, a method for classifying electroencephalographic (EEG) recordings with images as stimulation is introduced, which aims at selecting the target images. EEG recordings to be processed are referred to the onset of the test images with a single stimulation so as to avoid spending extra time on repeating images. Independent component analysis (ICA) is used to reduce the redundancy of EEG recordings, and wavelet packet (WP) analysis is efficient for dealing with the non-stationary character of brain activity. Feature vectors are extracted by a method that combines these two algorithms. The support vector machine is used as a classifier, carrying out the classification result. The experimental results demonstrate that the accuracy of this method's image classification is affected very little by different classifier parameters. The best result achieves 90% accuracy, which indicts it is feasible for classifying images with a single stimulation.
Schalk G., , Mellinger J., A Practical Guide to Brain-Computer Interfacing with BCI2000, London: Springer-Verlag, 2010.
Brunner Peter, , Ritaccio Anthony L., et al. Rapid communication with a ``P300'' matrix speller using electrocorticographic signals (ECoG), Frontiers in Neuroscience, February 2011, Volume 5.
Bogue R., Brain-computer interfaces: Controlled by thought, Industrial Robot, 37(2), 126-132, doi: 10.1108/0143991 10110188944.
Squidoo, A brief history of mind control technology, 2010, http://www.squidoo.com/historyofmindcontrol.
Hillyard SA, Imaging techniques: Event-related potentials (ERPs) and cognitive processing, New Encyclopedia of Neuroscience, 2009, 30: 13-18. doi: 10.1016/b978-008045046-9.00311-9.
Linden DE, The P300: where in the brain is it produced and what does it tell us? Neuroscientist, 2005, 11: 563-576, doi: 10.1177/1073858405280524.
Farwell, L.A., , Donchin, E., Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials, Electroenceph Clin Neurophysiol, 1998, 70(6), 510-523.
Yann, R., , Lotte, F., OpenViBE: An Open-Source Software Platform to Design, Test, and use brain-computer interfaces in real and virtual environments, Presence, 2010, 19(1), 35-53.
Hyvrinen A., The fixed-point algorithm and maximum likelihood estimation for independent component analysis, Neural Processing Letters, 1999, 10(1): 1-5.
Vapnik Vladimir N., The nature of statistical learning theory second edition[M], New York, Spring-Verlag.
Mallat S., A Wavelet Tour of Signal Processing, Second Edition, Beijing, China Machine Press.
Nima B.S., et al., EEG dataset from Swartz Center, UCSD, http://headit-beta.ucsd.edu/studies.
Mognon Andrea, et al., ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features, Psychophysiology, 48 (2011), 229-240.
Hugo G, et al., FastICA package, an ICA program implementing fast fixed-point algorithm, http://www.cis.hut.fi/projects /ica/fastica/.
Chih-Wei Hsu, et al., LIBSVM package, a library for Support Vector Machine, http://www.csie.ntu.edu.tw/∼ cjlin/libsvm.