## 1 Introduction
More and more often we can create algorithms by which it is possible to convert the image of movement into a control or executive signal, which a computer or other microprocessor system gives to various types of devices, such as robots or neurogaming programs [5; 15; 18; 19; 20; 21]. In [23; 24], the authors reviewed various types of classification algorithms, used in the design of BCI systems. They are mostly based on the measurements of EEG signals. According to the knowledge conveyed in the works, such algorithms can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, and other classifiers. This article presents research that was based on the analysis of not only the EEG signal but also the EMG. The literature contains research on the subject of simultaneous use of the properties of measurements of these signals. The article [10] presents a rehabilitation technique based on the exoskeleton of the lower extremities integrated with the human-machine interface (HMI). It was used to record and process multimodal signals collected using the brain-machine interface (BMI) based on foot motor imagery (MI) and multichannel electromyographic (EMG) signals recorded from the leg muscles. The investigated system was tested on healthy people operating the exoskeleton under various conditions. The tests show that it is possible to analyze up to 15 signals simultaneously in real time during the movement. The MI of the foot is extracted from the EEG signals (seven channels) using the event-related (de)synchronization effect. The whole is complemented by the cooperation of the aforementioned EEG signal with EMG measurements. They represent the intention to move, the control system can initiate and differentiate right and left leg movement with a high degree of reliability. The analysis of the coupling of electroencephalogram signals with electromyography in stroke has been successfully presented in [25]. Taking advantage of the knowledge that the couplings between EEG and EMG signals during motion control reflect the interaction between the cerebral motor cortex and muscles, an approach named variable scale symbolic transfer entropy (VS-STE) analysis was proposed. Post-stroke patients and healthy volunteers participated in studies involving motor skills (e.g., grasping with the hands). The proposed VS-STE was used to evaluate the strength of the cortical-muscular coupling. This coupling therefore occurred between the EEG signal measured from the motor cortex and the EMG signal measured from the upper limb in both the time and frequency domains. The results of the studies showed a greater potency of the bidirectional (EEG-to-EMG and EMG-to-EEG) VS-STE in stroke patients compared to healthy controls. In [26], the possibility of using EMG to detect the intention to extend the arm/wrist was tested. This was to launch robot-assisted training for people without residual movements. Detection of movement intent with the EMG detector was compared to the EEG-BCI sensorimotor rhythm using only the ipsilesional activity. The achieved results have shown that it is possible to use EMG to detect the intention to move with a serious impairment of motor abilities. The probability of detecting EMG when the patients tried to move was higher than at rest. Many patients had strong EMGs in the finger/wrist extensors. It can be seen in the conclusions that some severely affected patients may benefit from EMG-based assisted therapy. Compared to the EEG, the EMG interface requires less preparation time, which is easier to use and maintain, and has a more compact size. In [27], authors investigated hybrid BCIs, coupling joystick data, EEG and EMG in the case of severe motor disabilities. The method of data processing and classification to detect right and left hand movements is presented. The EMG modality is well suited to patients with Duchenne Muscular Dystrophy (DMD).