Prototype design for bidirectional control of stepper motor using features of brain signals and soft computing tools

Abstract

The brain-computer interface (BCI) plays a significant role in supporting specially-abled people to control devices with the brain signals or electroencephalogram (EEG). The proper functioning of BCI systems requires classification algorithms to distinguish between different tasks based on the features extracted from EEG. Motivated by the requirement of designing a reliable BCI with reduced memory and computational cost, this work proposes an EEG controlled stepper motor drive-based aiding device. The drive system is executed in real-time based on the processing and classification of EEG. The scheme initiates with the extraction of discriminatory features from raw time-domain EEG signals using higher-order statistics (HOS) and phase locking value (PLV).Further, with extracted feature vector, the present study contemplates the operation of backpropagation neural network (BPNN), k-Nearest neighbours (k-NN), and support vector machine (SVM). Signal classification by SVM in conjugation with nonlinear principal component analysis (NLPCA) is implemented to reduce the feature vector dimension. Average classification accuracy of 82.70% and 80.46% is achieved using NLPCA with SVM for PLV and HOS features. The classifier output is utilized to spin the stepper motor clockwise and anticlockwise as needed. The control of stepper motor uses the classifier output and hence the brain signals are implemented on a laboratory-developed digital test bench comprising of TI TMS320F28379D launchpad DSP board and MITSUMI stepper motor. The validation of the proposed scheme for different signals reflects its effectiveness in combining the software and hardware aspects required for realizing an actual BCI system for real-time settings.