Control of artificial limb using EEG and EMG - a review

Due to some diseases or spinal cord injury, sensory, motor and autonomous function for the limb movement is completely destructed. BCI (Brain computer Interface) provides a new communication pathway for those patients. Imagination of limb movements is used to operate a BCI. With analysis of acquired EEG signal due to motor imagery controlling of an artificial limb is possible. For this technique motor imagery EEG signal is classified and the classified part is fed to a controller to execute exactly that movement. State feedback PI controller can be used to control an artificial limb. With help of this controller not only position but also velocity can be controlled. In this paper, a simulated model of EEG driven artificial limb control using state feedback PI controller is presented. For this study, EEG data for motor imagery was taken from five healthy subjects. The wavelet coefficients are calculated from that EEG signals as features and the obtained features are classified by QDA classifier to determine the part of the limb the user wants to move. The initial and target position are fed to the controller and the controller move the artificial limb to reach the target position at the classified direction. The overall control procedure is done using Matlab 7.6.

2020

1-3Student, Dept. of Computer Engineering, Terna Engineering College, Navi Mumbai, India. 4Professor, Dept. of Computer Engineering, Terna Engineering College, Navi Mumbai, India. ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract The field of prosthetics has showed a significant improvement over last few years, due to advancement in technologies. However, they have certain problems either with being really expensive, does not provide full motor functions, may require surgical approach or does not look like an arm. This project describes how the Brain waves can be used to control a prosthetic arm using Brain Computer Interface (BCI). The BCI system consist of Electroencephalogram (EEG) sensors placed on the headset to capture the brain waves, which will be extracted using Thinkgear library in MATLAB. The Brain signal act as command signals and transmitted to microcontroller. This comma...

This paper describes a brain controlled robotic leg which is designed to perform the normal operations of a human leg. After implanting this leg in a human, the leg can be controlled with the help of user's brain signals alone. This leg behaves similar to a normal human leg and it can perform operation like walking, running, climbing stairs etc. The entire system is controlled with the help of advanced microcontrollers and digital signal

Journal of emerging technologies and innovative research, 2019

Necessity is the mother of all inventions. BrainThe Master of our body generates signals in accord with our thoughts and decrees every part to perform the desired actions. This paper is a boon to the amputees since it can decrease their encumber. This paper targets in trapping the signals by the use of Brain wave sensor (Sensors that are attached to the scalp in order to monitor the Brain Wave activity in different parts of the brain) and feed the signals to the so designed artificial hand. Adroit limb is different from the already existing ones. It can encompass activities like peeling; feel things as our normal human hand. The existing models can provide only support but the proposed prototype for this paper can respond to External Stimulus. Brain waves are obtained from a special analysis of EEG (Electro Encephalo Gram). These brain waves show us the brain's response to an external stimulus or event. Brain activity before, during, and after a stimulus presentation is recorded...

The aim of this study is to develop a low cost prosthetic hand for the people of third world countries. In most cases the prosthesis requires surgery and very costly sensors which is not feasible for poor people. So a new approach is introduced in our study where Electroencephalography sensor and Electromyography sensor is used as a combination to receive necessary brain and muscle signals respectively to operate the artificial hand. Since both types of sensors do not require any direct contact with the nerves rather skin contact only, this study has shown potential for surgery-free implantation of the hand. In our study a new linkage mechanism is utilized for controlling each finger with a single motor. Our study showed that using attention level of human mind, the prosthetic hand can be operated to grab and release objects of different size and shape within a certain weight limit. Also force exerted and finger patterns can be controlled with muscle signals.

Sensors, 2021

Creating highly functional prosthetic, orthotic, and rehabilitation devices is a socially relevant scientific and engineering task. Currently, certain constraints hamper the development of such devices. The primary constraint is the lack of an intuitive and reliable control interface working between the organism and the actuator. The critical point in developing these devices and systems is determining the type and parameters of movements based on control signals recorded on an extremity. In the study, we investigate the simultaneous acquisition of electric impedance (EI), electromyography (EMG), and force myography (FMG) signals during basic wrist movements: grasping, flexion/extension, and rotation. For investigation, a laboratory instrumentation and software test setup were made for registering signals and collecting data. The analysis of the acquired signals revealed that the EI signals in conjunction with the analysis of EMG and FMG signals could potentially be highly informati...

IFMBE Proc, 2005

A BCI real-time system using Steady-State Visual Evoked Potentials generated by stimulus via a standard CRT-computer screen is discussed. The system is working in the synchroneous mode, i.e. using visual input-stimuli. Detection performance is within the range [58-100%] thus rather large differences between subject performances are seen. Also results from basic EEG-research investigating the cortical modulation of movement-related EEG-parameters are presented. The long-term aim is here to recognize cortical activity related to force, velocity and direction in the EEG with the purpose of more advanced orthosis control in rehabilitation.

Neuroscience Letters, 2005

This case study demonstrates the coupling of an electroencephalogram (EEG)-based Brain-Computer Interface (BCI) with an implanted neuroprosthesis (Freehand ® system). Because the patient was available for only 3 days, the goal was to demonstrate the possibility of a patient gaining control over the motor imagery-based Graz BCI system within a very short training period. By applying himself to an organized and coordinated training procedure, the patient was able to generate distinctive EEG-patterns by the imagination of movements of his paralyzed left hand. These patterns consisted of power decreases in specific frequency bands that could be classified by the BCI. The output signal of the BCI emulated the shoulder joystick usually used, and by consecutive imaginations the patient was able to switch between different grasp phases of the lateral grasp that the Freehand ® system provided. By performing a part of the grasp-release test, the patient was able to move a simple object from one place to another. The results presented in this work give evidence that Brain-Computer Interfaces are an option for the control of neuroprostheses in patients with high spinal cord lesions. The fact that the user learned to control the BCI in a comparatively short time indicates that this method may also be an alternative approach for clinical purposes.

Sensors & Transducers, 2008

The aim of this paper is described a human machine interface using Electromyogram signal to artificially control the limb movement usually called fictional Electrical Stimulation and Rehabilitation. Functional electrical stimulation (FES) is used widely in rehabilitation to restore motor functions for paralyzed patients. Each muscle fiber has a potential and motor unit action potential generated by construction of muscle is studied and corresponding actuation is provided to robotics arm.

Frontiers in Robotics and AI, 2016