Brain computer interface controlled automatic electric drive for neuro-aid system

Abstract Brain-computer interface (BCI) systems are used to establish communication pathways between the brain signals and a peripheral device. One such pathway can be fashioned between these signals and an electric drive to be further used for any neuro-aid application. Brain signals, which in this study are the electroencephalogram (EEG) signals from a specified focal area, get synchronized and desynchronized upon the occurrence of different motor imagery (MI). Some specific features of the neuronal brain activities occurring during MI can be extracted to get the relevant information from them. This paper proposes an algorithm based on time-varying signed distance (TSD) to obtain well-separated EEG data for the process of feature extraction. Post TSD application, power spectral density (PSD) based features were extracted, followed by using the measure of cross-correlation to select the most dominant PSD based features. A quadratic discriminant analysis (QDA) is adopted to classify the test data and to generate further control signals for regulating the motion of brushless DC (BLDC) motor. The rotation of the motor is governed by the different classes of MI data. For the experimental dataset, the accuracy of the classifier was found to be 83.3 %, 71.6 %, and 78.3 %, respectively, for three different subjects. The case corroborates the efficacy of the proposed algorithm for practical applications. Experimental validation of the proposed system is presented on a 1 kW brushless DC (BLDC) motor drive system.