Abstract
Brain machine interface (BMI) has been proposed as a novel technique to control prosthetic devices aimed at restoring motor functions in paralyzed patients. In this paper, we propose a neural network based controller that maps rat’s brain signals and transforms them into robot movement. First, the rat is trained to move the robot by pressing the right and left lever in order to get food. Next, we collect brain signals with four implanted electrodes, two in the motor cortex and two in the somatosensory cortex area. The collected data are used to train and evaluate different artificial neural controllers. Trained neural controllers are employed online to map brain signals and transform them into robot motion. Offline and online classification results of rat’s brain signals show that the Radial Basis Function Neural Networks (RBFNN) outperforms other neural networks. In addition, online robot control results show that even with a limited number of electrodes, the robot motion generated by RBFNN matched the motion generated by the left and right lever position.
Highlights
IntroductionThe reason being, that brain signals recorded with multiple electrodes pasted on scalp or implanted inside the brain, are regarded as a communication channel with high potential for the control of prosthetic devices [1,2]
During the last two decades, brain machine interface (BMI) has received increasing attention.The reason being, that brain signals recorded with multiple electrodes pasted on scalp or implanted inside the brain, are regarded as a communication channel with high potential for the control of prosthetic devices [1,2]
The classification accuracy achieved by the Radial Basis Function Neural Networks (RBFNN) is higher than that of similar works shown in [14]
Summary
The reason being, that brain signals recorded with multiple electrodes pasted on scalp or implanted inside the brain, are regarded as a communication channel with high potential for the control of prosthetic devices [1,2]. This could be very useful to patients with disabilities, severe motor dysfunctions, locked-in syndrome, etc. Various BMI works, have investigated the control of robotic devices using animal or human brain activity. Due to high risk and complicated surgical procedures, human invasive BMI is exploited less than animal BMI [11]
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