Effective Brain–Computer Interface Based on the Adaptive-Rate Processing and Classification of Motor Imagery Tasks

Abstract

In the context of cloud-based mobile healthcare systems, continuous multichannel electroencephalogram (EEG) signals acquisition, processing, transmission, and analysis is required. The conventional BCI (Brain to computer interface) systems are time-invariant and can have the disadvantage of capturing redundant information. It leads to a waste of system ressources and power consumption. In this context, this chapter presents an original approach of realizing the adaptive rate signal acquisition and processing chain for the cloud-based BCI. The objective is to achieve a real-time compression gain in order to attain effective EEG signal processing, transmission, and analysis in the context of realizing a proficient cloud-based BCI framework. The signal is enhanced in this study by the use of the method of adaptive rate filtering. Attributes are derived from the conditioned signal using a hybrid method. Afterward, robust classifiers are used to classify different intended EEG signal classes. The performance of the system is studied by using a standard data set of motor imagery tasks. The devised approach is able to achieve appreciable compression gain and computational improvement when compared with the traditional methods. The system also produces a good classification accuracy.