# Current State of Robotics in Hand Rehabilitation after Stroke: A Systematic Review
## 2 Materials and Methods
### Control Strategy of Hand Rehabilitation Robot
#### 2.3.2 Bioelectric Signal Control  
Surface electromyography (sEMG), electroencephalography (EEG), and electrooculography (EOG) are mainly used for the interactive control of hand rehabilitation robots. Since these signals are acquired in a non-invasive manner, surface EMG and EEG are obtained in an operable way that does not require medical expertise, and their performance can be guaranteed. sEMG signal control: Surface electromyographic signals (sEMG), acquired by non-invasive electrodes affixed to the skin surface, contain information about the motor state and motor commands of the neuromuscular system [87,88], reflecting the generation and propagation of action potentials from muscle motor units [89]. sEMG is currently widely used in prosthetic control, rehabilitation robot control, exoskeleton robot control, teleoperated robots, virtual reality, etc., [90,91,92,93] using machine learning algorithms The sEMG signal is used to decode the human motor intent and map it to the control output, which is used to establish an information channel between the patient and the robot to obtain biofeedback control during rehabilitation training [89,94,95,96,100,101]. The sEMG-generated EMG control allows the patient to participate in the actual control of the device, which is important for increasing the patient's awareness of active participation [101]. In the study by [PERSON] et al. [102]. It was emphasized that the degree of muscle changes due to stroke depends on the severity of the stroke and its effect on neuromuscular activity, and that accurate lower limb muscle selection is crucial for identifying stroke impaired gait impairments, as well as for hand muscles. Machine learning and deep learning methods applied to EMG detection techniques can effectively classify EMG pattern recognition, and since this review is biased towards an overview of hand rehabilitation robots, in general, the review of the bioelectric signal part will be systematically summarized in a subsequent study. Unilateral limb dysfunction caused by stroke is one of the post-stroke symptoms and providing rehabilitation training to post-stroke patients can improve the patient's motor ability and thus the ability to perform daily activities. Bilateral training is a rehabilitation strategy based on natural limb coordination [103]. Bilateral training involves manipulating both hands together, with the participant using both hands to work together to accomplish the target task. It has been shown that simultaneous movement of both limbs helps the neuromuscular system to restore a degree of stability and improve the efficiency of the use of the damaged limb [104]. Training patients with a two-handed task improves the efficiency of grasping movements on the neurologically impaired side, accompanied by a reconfiguration of neural networks in the brain of the impaired hemisphere [105]. In healthy individuals, corticomotor control of spontaneous hand movements is derived from contralateral cortical areas because of the contralateral control of left and right-hand movements in humans; after stroke, the role of uncrossed fibers in the cerebrospinal pathway is redirected and the balance of limb control is shifted from the injured hemisphere to the contralateral hemisphere [106]. Therefore, bilateral training can contribute to motor function rehabilitation after stroke [107]. EEG signal control: The brain is the source of all human movement and thought, consisting of ten billion nerve cells that continuously process information obtained from the outside and efficiently send feedback instructions to various organs and structures of the body. Since the first recording of the human electroencephalogram (EEG) by German scientists in 1929 [108], the development of brain-computer interface technology has never stopped, and the dream of using EEG signals for control and communication with external devices has never ceased. However, the EEG signals recorded for the first time by German scientists are quite random and non-linear.