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This study aimed to find an optimal feature-extraction method for real-time electroencephalography-based navigation. A comparative study of promising methods was done on two-class motor-imagery data. A novel combination of Hjorth parameters and Welch's-power-spectral-density produced the highest two-class accuracy of 0.84 across five subjects. The method is computationally inexpensive and provides short training and execution times that best suit real-time applications. The method yielded an average accuracy of 0.66 on three-class data. The real-time feasibility of the method was tested by implementing an object-retrieving robot and running a navigation experiment. The robot successfully navigated a three-circuit maze using three commandsright, left, and forwardand automatically retrieved the desired object. Along with being ideal for real-time applications, this method utilized only eight electrodes, making it compatible with cheap, portable BCI devices. The method and the application-framework can find commercial utility in the field of healthcare for the elderly and physically-handicapped.

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