**
Summary
**
In the past few decades, the applications of biorobots, such as exoskeletons, prostheses, and robotic wheelchairs, have evolved from machines in science fiction to almost commercial products. Although there are still some challenges with electromyography (EMG) signals, the progress in using EMG signals to control such biorobot applications is huge.
Similarly, the recent trends and attempts to develop control methods based on electroencephalogram (EEG) have also shown the potential of this field in the field of modern biorobots, but the control methods based on EEG need to be improved.
A new method that combines these two control methods, uses the advantages of each system and reduces the disadvantages, so it may be a very promising system. This article reviews the hybrid fusion control methods based on EMG and EEG that have been tried or developed in the field of biorobots in recent years, and proposes some potential future directions.
01Introduction to control methods
The latest developments in the field of biorobots have contributed to improving the quality of life of a series of people in many ways. For people who are physically weak, disabled or injured, applications or equipment such as prosthetics, exoskeletons, teleoperation robots, and smart wheelchairs give them Life brings some hope. However, controlling these devices requires complex techniques or methods because they usually interact with human users.
The main requirements for these devices, such as accuracy, long-term reliability and safety are paramount. Therefore, in order to meet these requirements, many control methods have been proposed, each of which uses a different type of input signal.
Electromyography (EMG) can directly reflect human movement intention or user's muscle activity. Therefore, electromyography (EMG) has always been one of the most commonly used biological signals in the control methods of biological robots. Many examples, such as wheelchairs , Prostheses, and exoskeleton/orthotics have all shown the effectiveness of muscle signals based on EMG.
EMG-based methods cannot be used as input. For example, people with complete upper limb paralysis may not be able to use equipment such as exoskeleton because it is difficult to obtain control signals from the muscles of the paralyzed limb.
On the other hand, with the advancement of technology, BCI (brain computer interface) or BMI (brain machine interface) has attracted the attention of the field of biorobots. The brain-computer interface can open up new ways to directly decode the user’s brain signals to control devices such as prostheses, exoskeletons or wheelchairs; for example, even if the user’s limbs cannot perform any sufficient movement, he can still generate commanding brain signals. These signals can be used for this brain control interface to drive the exoskeleton.
Among a variety of brain signal acquisition methods, electroencephalography (EEG) is considered a non-invasive and convenient method suitable for actual systems. Various attempts to implement interfaces based on EEG signals can be found in applications such as wheelchairs, artificial limbs, and exoskeletons. However, because EEG has difficulties such as low reliability, low accuracy, poor user adaptability, and low data transmission rate, BCI/BMI that only uses EEG signals as the main input cannot be fully accepted in biorobot applications.
In order to overcome the problems of EEG and EMG-based control methods, combining the two systems, using the advantages of each signal and reducing their limitations, may be a method. For example, in the case of prosthetic limb control, some muscles required by the EMG-based method may not be available. In this case, the EEG signal can be used to compensate for the missing EMG signal.
In addition, in examples such as exoskeletons, some muscles required for the EMG signal may be disconnected or paralyzed, or some nerves connected to the required muscles may be disconnected. In this case, EEG can also be used to compensate for the missing EMG signal. Even if all the muscles needed for EMG are available, EEG can still be used to eliminate the effects of fatigue or unexpected tremors.
This article mainly reviews the hybrid/fusion EEG-EMG interface proposed in biorobot applications so far, and determines the important design features, advantages and disadvantages of these systems. Although there are many comments on EMG-based control methods or EEG-based control methods (using BCI), it is difficult to find any in-depth overview of EEG-EMG hybrid methods currently in biorobot applications.
In fact, although some comments on hybrid BCI have been published, there is no article detailing the hybrid EEG-EMG control method, especially for the application of biorobots. A timely written review paper combined with EEG and EMG methods to control the application of this biorobot technology will not only help to determine the current status of the research field, but also for anyone interested in launching/developing such systems People provide information.
In addition to reviewing the application of hybrid EEG-EMG-based methods in biorobots, we also discussed the application of EMG-EEG hybrid control methods in biorobots, and we will also propose several possible future directions.
"Shh! Quietly insert a small commercial"
Figure 1 BCIduino physical image The
specific parameters of the BCIduino 8-channel EEG amplifier are as follows:
Input impedance: 1TΩ
Input bias current: 300pA
Input reference noise: 1μVpp
Sampling rate: 250 Hz/500Hz
Common mode rejection ratio: -110dB
adjustable gain amplification factor: 1, 2, 4, 6
, 8, 12, 24 resolution: 24-bit ADC, accuracy up to 0.1μV
power consumption: 39mW in normal operation, as low as only 10μW in standby
using rechargeable lithium battery power supply, further reducing External interference.
Size: 50mm*50mm (physical measurement, with slight errors)
Figure 2 The data waveform of BCIduino in an ordinary noisy environment and floating state, and no other interference can be observed
. Figure 3 The data waveform of OpenBCI in an ordinary noisy environment, floating state (The measurement environment, measurement time, and software filter setting parameters are the same as those in Figure 2BCIduino)
Add management to get more
This article is organized or written by the BCIduino brain-computer interface open source community. The BCIduino brain-computer interface community was initiated and established by masters and doctors from Beihang University, Cornell University, Peking University, Capital Medical University, etc. Welcome to join the community by scanning the code and note "BCI", and also welcome to purchase BCIduino EEG modules (some Search for treasure)