A Brain-Wave-Actuated Small Robot Car Using Ensemble Empirical Mode Decomposition-Based Approach

Abstract

An ensemble empirical mode decomposition (EEMD)-based approach was developed to extract steady-state visual evoked potentials (SSVEPs) for wireless handling of a small robot car. Three visual stimuli, flickering at 13, 14, and 15 Hz, were displayed on a liquid crystal display monitor to induce user's SSVEPs. The induced SSVEPs were used to control three movement functions (forward, left, and right) of the small robot car. Users gazed at one chosen visual stimulus at one time, and the induced SSVEP was recognized to activate the desired movement function. In this paper, all subjects were requested to handle the small robot car to complete an S-shaped course four times. The proposed system utilized only one electroencephalography (EEG) channel placed at the Oz position. The acquired EEG signals were first segmented into 1-s epochs, and each epoch was then decomposed by EEMD into a series of oscillation components, denoted as intrinsic oscillatory functions (IOFs), representing multiscale features of the recorded signal. The SSVEP-related IOFs were then recognized using a matched filter detector (MFD), including a matched filter demodulator and an amplitude detector. The visual stimulus, which contributed maximum power to the MFD, was recognized as the gazed target. In this paper, all subjects could actuate the small robot car using the proposed EEMD-based brain computer interface system to complete an S-shaped course four times; the mean execution time, number of valid detections, and command transfer interval over the 11 subjects were 84.5 s, 51.13 commands, and 1.65 s/command, respectively.