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Comparative Study
. 2008 Mar 26:5:10.
doi: 10.1186/1743-0003-5-10.

Human-machine interfaces based on EMG and EEG applied to robotic systems

Andre Ferreira  1 , Wanderley C Celeste, Fernando A Cheein, Teodiano F Bastos-Filho, Mario Sarcinelli-Filho, Ricardo Carelli
Affiliations
Comparative Study

Human-machine interfaces based on EMG and EEG applied to robotic systems

Andre Ferreira et al. J Neuroeng Rehabil. .

Abstract

Background: Two different Human-Machine Interfaces (HMIs) were developed, both based on electro-biological signals. One is based on the EMG signal and the other is based on the EEG signal. Two major features of such interfaces are their relatively simple data acquisition and processing systems, which need just a few hardware and software resources, so that they are, computationally and financially speaking, low cost solutions. Both interfaces were applied to robotic systems, and their performances are analyzed here. The EMG-based HMI was tested in a mobile robot, while the EEG-based HMI was tested in a mobile robot and a robotic manipulator as well.

Results: Experiments using the EMG-based HMI were carried out by eight individuals, who were asked to accomplish ten eye blinks with each eye, in order to test the eye blink detection algorithm. An average rightness rate of about 95% reached by individuals with the ability to blink both eyes allowed to conclude that the system could be used to command devices. Experiments with EEG consisted of inviting 25 people (some of them had suffered cases of meningitis and epilepsy) to test the system. All of them managed to deal with the HMI in only one training session. Most of them learnt how to use such HMI in less than 15 minutes. The minimum and maximum training times observed were 3 and 50 minutes, respectively.

Conclusion: Such works are the initial parts of a system to help people with neuromotor diseases, including those with severe dysfunctions. The next steps are to convert a commercial wheelchair in an autonomous mobile vehicle; to implement the HMI onboard the autonomous wheelchair thus obtained to assist people with motor diseases, and to explore the potentiality of EEG signals, making the EEG-based HMI more robust and faster, aiming at using it to help individuals with severe motor dysfunctions.

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Figures

Figure 1
Figure 1
Signals adopted in different Human-Machine Interfaces, and the corresponding levels of capacity.
Figure 2
Figure 2
The magnitude spectrum of a normal EEG signal.
Figure 3
Figure 3
Basic structure of a BCI.
Figure 4
Figure 4
The 10–20 International System for placing electrodes.
Figure 5
Figure 5
The structure of the proposed HMI.
Figure 6
Figure 6
The electronic board used to represent the robot working space.
Figure 7
Figure 7
The eye-blink detection scheme.
Figure 8
Figure 8
ERD and ERS observed in alpha band.
Figure 9
Figure 9
Energy increase during an ERS.
Figure 10
Figure 10
Simulated environment in which the mobile robot navigates.
Figure 11
Figure 11
Pioneer 2-DX robot operated through EEG signals.
Figure 12
Figure 12
Illustrating the whole system.
Figure 13
Figure 13
The manipulator Bosch SR-800.
Figure 14
Figure 14
The local Graphical Interface.
Figure 15
Figure 15
The path generated by the system.
Figure 16
Figure 16
Result of the experiment with unpredicted obstacle distant of the robot's navigation path.
Figure 17
Figure 17
Number of individuals that managed to learn how to use the BCI versus the training time required (in minutes).
See this image and copyright information in PMC

References

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    1. Mouriño J. PhD thesis. Universitat Politècnica de Catalunya, Barcelona, Spain; 2003. EEG-based Analysis for the Design of Adaptive Brain Interfaces.
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