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Design and evaluation of a P300-ERP based BCI system for real-time control of a mobile robot | IEEE Conference Publication | IEEE Xplore

Design and evaluation of a P300-ERP based BCI system for real-time control of a mobile robot


Abstract:

With the development of Brain-Computer Interface (BCI) systems people with motor disabilities are able to control external devices using their thoughts. To control a devi...Show More

Abstract:

With the development of Brain-Computer Interface (BCI) systems people with motor disabilities are able to control external devices using their thoughts. To control a device through BCI, brain activities of the user must be accurately translated to meaningful commands and a design of appropiate BCI paradigms play important roles in such tasks. This work presents a design and evaluation of a BCI system that is based on P300 Event-Related Potentials (ERP) in order to control a mobile robot platform into four directions (left, right, front, back). The ultimate goal of this research is to provide convienient way of controlling a mobile robot as an assistive home technology for disabled people. Low cost EPOC Emotiv headset was used in the BCI system to acquire brain signals with a Jaguar 4x4 Wheel robot as a control platform. We discuss a set of signal processing steps employed in detail and the utility of a regularized logistic regression classifier to detect visual stimuli induced P300 ERPs and, to control the Jaguar robot.
Date of Conference: 09-11 January 2017
Date Added to IEEE Xplore: 20 February 2017
ISBN Information:
Conference Location: Gangwon, Korea (South)

I. Introduction

One of the latest emerging technologies in the field of robotics and artificial intelligence is the system of brain- computer interfaces (BCI), also referred as Brain-Machine Interface (BMI), Mind-Machine Interface (MMI), or direct neural interface [1]. The BCI technology consists of the hardware that is able to recognize brain signals and the software that can effectively process these data [2]. As a result, successfully implemented BCI gives opportunity to control physical objects by decoding electrophysiological signals generated by human brain activities. Essentially, these kinds of signals are extracted from neurons in cortex when the electrodes are placed on a human scalp. These neural signals become useful as an input to measuring electronic devices due to the local field potential, which creates corresponding oscillatory wave [3]. After received brain signals are amplified, they are processed by analog to digital converter such that computer can perform further analysis. There are five commonly referred stages that neural signals are processed through: signal acquisition with preliminary noise reduction, signal preprocessing or enhancement, feature extraction, classification and output control interface [2]. Based on the communication channel between brain and the computer, this method of controlling devices without muscles and peripheral nerves can be implemented to solve problems with mobility impairment. For example, brain-actuated wheelchair is one of the most promising applications of BCIs that can help disabled people to move and interact with surrounded world through the intelligent robotics system [4]. In addition, various other invasive BCI systems have been developed to control external devices, e.g. computer cursors [5] and robotic prostheses/orthoses [6]. Moreover, in recent studies, BCIs have been used to control lower-body [7] and upper-body exoskeletons [8] for stroke and paraplegic recovery and rehabilitation via non-invasive approaches [9].

References

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