Feature extraction and classification of imagined motor movement electroencephalogram signals

Brain–Computer Interface (BCI) establishes a communication channel between brain and external world. BCI can control numerous applications, such as controlling a cursor on computer screen, movement of a robotic arm or a wheel chair and many more. The efficiency and accuracy of BCI systems completely relies on efficient preprocessing and classification algorithms. In present work, the reliability of a BCI has been analysed, which is implemented using Electroencephalogram (EEG) signals, recorded from motor imagery, for imagination of three different mental tasks: left fist blink, right fist blink and both fists blink. The recorded EEG signals were primarily filtered out by a low pass Butterworth filter and further preprocessed using multiscale principal component analysis. Statistical parameters have been calculated from preprocessed EEG signals as features. For classification of the EEG signals, support vector machine model is employed.

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