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

Journal of NeuroEngineering and Rehabilitation Human-machine interfaces based on EMG and EEG applied to robotic systems Andre Ferreira 1 Wanderley C Celeste 1 Fernando A Cheein 0 Teodiano F Bastos-Filho 1 Mario Sarcinelli-Filho 1 Ricardo Carelli 0 0 Institute of Automatics, National University of San Juan , Av. San Martin, 1109-Oeste, 5400, San Juan , Argentina 1 Department of Electrical Engineering, Federal University of Espirito Santo , Av. Fernando Ferrari, 514, 29075-910, Vitoria-ES , Brazil 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. - Background Electro-biological signals have become the focus of several research institutes, probably stimulated by the recent findings in the areas of cardiology, muscle physiology and neuroscience, by the availability of more efficient and cheaper computational resources, and by the increasing Electrical signals coming from different parts of the human body can be used as command signals for controlling mechanical systems. However, it is necessary that the individual in charge of controlling such devices be able to intentionally generate such signals. It is also necessary that the interface adopted (the Human-Machine Interface HMI) can "understand" and process such signals, setting the command that better fits the wish of the individual. Then, an HMI can be used to improve the capacity of movement of individuals with motor dysfunctions, using, for example, a robotic wheelchair to carry them. Many electro-biological signals can be used in connection with HMIs. Some of the more commonly adopted signals are the Electro-Myographic (EMG) signal, the ElectroOculographic (EOG) signal and the Electro-Encephalographic (EEG) signal. This work presents results related to the use of EMG and EEG signals. The use of EOG signal is still incipient in the studies we have developed so far. EMG signals are generated by neuromuscular activity, with signal levels varying from 100 V to 90 mV with frequency ranging from DC to 10 kHz. Such signals have a standard behavior, which is an important feature to take into account when designing an HMI interface to link an individual with motor dysfunction and a mechanical device. Furthermore, the signal level corresponding to EMG signals is higher when compared to the level corresponding to EEG signals, thus being easier to discriminate its level. In other words, if the individual using the HMI generates normal EMG signals, this kind of signal should be adopted. However, there are some problems inherent to the use of EMG signals. Considering that the assisting technology we deal with in this work is also directed to people with neuromotor disabilities, some muscle spasms, for example, can take place, which represent a serious problem (unless the HMI is robust enough to reject such disturbances) when using EMG signals to control mechanical devices. Severe neuromotor injuries can also cause loss of muscle mobility, which makes impossible to use any kind of EMG-based control to assist individuals with such diseases. Thus, other communication channels (in this scenario other electro-biological signals) should be explored to avoid this kind of problem. As presented in Figure 1, brain signals can be a good solution when EMG and EOG signals are not available, as when assisting individuals with muscle spasms or locked in syndrome [3]. The EEG signal corresponds to the electrical potential due to brain (neuron) activity, and can be acquired on the scalp (signal amplitude usually under 100 V) or directly on the cortex (called Electrocorticography ECoG), the SFiiggnuarlsea1dopted in different Human-Machine Interfaces, and the corresponding levels of capacity Signals adopted in different Human-Machine Interfaces, and the corresponding levels of capacity. surface of the brain (signal having about 1 - 2 mV of amplitude). The frequency band of normal EEG signals is usually from a little bit above DC up to 50 Hz (see Figure 2). Although EEG signals were initially used just in Neurology and Psychiatry, mainly to diagnose brain diseases as epilepsy, sleep disorders and some types of cerebral tumors, many research groups are now using them as a communication channel between a person's brain and electronic machines, in order to develop systems to improve his life condition. The main point of this idea is the Human-Machine Interface (HMI), also called a BrainComputer Interface (BCI), a system capable to acquire the EEG signal, to extract features there embedded, to "understand" the intention manifested by the user and to control electronic devices such as a PC, a robot or a wheelchair. In addition, if the objective is to develop a portable and embedded BCI, low cost, small size, small weight and portability are very important advantages of systems based on the EEG signal when compared to other ways to register brain activity [4]. Other advantages of using EEG signals are: they have good temporal resolution and allows extracting features enough to control electronic devices (since appropriate signal processing methods are used). A BCI, as a HMI, follows the basic structure presented in Figure 3, which is composed of (...truncated)