EEG patterns for driving wireless control robot

Abstract

Electroencephalogram (EEG) study has significant use for disable people since many people with severe motor disabilities require alternative method for communication and control. Normally this people have normal brain function that can be used to control assistive devices. Therefore this study presents preliminary results of EEG pattern for driving wireless control robot. The objective was to obtain optimum scalp location. For each task, EEG signals were recorded from 19 scalp location. Four recorded tasks were investigated and divided into Task1, Task2, Task3 and Control. All tasks were preceded by Control task. Fast Fourier Transform (FFT) was used to analyze the recorded signals. The difference in power between task and control was analyzed. Results showed that Pz and P4 are the best location for Task1, T 4 and P3 for Task2 and Task3 respectively. All these occurred in delta frequency band. © 2011 Springer-Verlag.