Affiliations: [a] Department of Electrical Engineering, AMU, Aligarh, India
| [b] Division of Instrumentation and Control Engineering, NSIT, Delhi
Correspondence:
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Corresponding author. Nidal Rafiuddin, Department of Electrical Engineering, AMU, Aligarh, India. E-mail: nidal.rafi@gmail.com.
Abstract: Brain-computer interface may be delineated as the merger of machine and software through which brain activity is allowed to govern a peripheral device or computer. The major aim is to aid a critically paralyzed person to live a normal healthy life. This arrangement passes over numerous stages which include data acquisition, feature extraction, data classification and control. The present work emphasizes the use of selective wavelet based features and classifies them using an artificial intelligence based technique namely support vector machine for wrist movement in four different directions. The data base used is the data set-3 of Brain-computer interface competition-4, which pertains to MEG signals acquired from two healthy subjects performing wrist movement in four different directions. The signal was processed using both wavelet packet transform and discrete wavelet transform and thereafter statistical features were extracted. The best discriminating features were selected after ranking all the extracted features using Principle component analysis. These features were then fed to the support vector machine based classifier for classification. The accuracy achieved is better than most reported in theliterature.
Keywords: BCI, MEG, support vector machine, wavelet packet transform, discrete wavelet transform
