Design and evaluation of a motor imagery electroencephalogram-controlled robot system

Abstract

Brain-computer interface provides a new communication channel to control external device by directly translating the brain activity into commands. In this article, as the foundation of electroencephalogram-based robot-assisted upper limb rehabilitation therapy, we report on designing a brain-computer interface-based online robot control system which is made up of electroencephalogram amplifier, acquisition and experimental platform, feature extraction algorithm based on discrete wavelet transform and autoregressive model, linear discriminant analysis classifier, robot control board, and Rhino XR-1 robot. The performance of the system has been tested by 30 participants, and satisfactory results are achieved with an average error rate of 8.5%. Moreover, the advantage of the feature extraction method was further validated by the Graz data set for brain-computer interface competition 2003, and an error rate of 10.0% was obtained. This method provides a useful way for the research of brain-computer interface system and lays a foundation for braincomputer interface-based robotic upper extremity rehabilitation therapy.

Design and Evaluation of a USB Isolator for Medical Instrumentation

“…However, the usage of medical grade isolation transformers reduces the portability in the PC and the batteries limit the time of use of ME. Other designs use isolation amplifiers [11]- [13], isolation transformers [14], [15], or sensors that use insulating materials [16]- [18] in the signal acquisition stage of the ME. Although these provide electrical protection for the patient, the operator is at risk if there are no other isolation media to protect him/her from electric shock.…”

“…Different isolation methods to provide electrical protection to the patient in those MEs integrated into the PCs have been applied. Some of these isolate the power supply stage of the PC [8]- [10], others isolate the signal acquisition stage in the ME [11]- [18], and others perform an isolation in the communication stage between the PC and the ME [19]- [27]. However, isolation in the PC's power supply could reduce the portability of the PC (e.g., medical transformers or medical uninterruptible power system) and if isolation in the acquisition stage is used as an alternative, there could be a risk of electric shock for the operator.…”

Driving Anger States Detection Based on Incremental Association Markov Blanket and Least Square Support Vector Machine

“…Particularly, as the designed experiments were conducted in real traffic environment, raw EEG data was mixed with numerous noises, among which high frequency noise is due to atmospheric thermal noise while low frequency noise is mainly due to eye movements, respiration and heart beats. Then, the high and low frequency noise were filtered through band pass filter with cutoff frequencies of 0.5 Hz and 35 Hz, as well as a method based on independent component analysis [38].…”

A Novel Smart Motor Imagery Intention Human-Computer Interaction Model Using Extreme Learning Machine and EEG Signals

“…In addition, the team used the same method to implement a wheelchair control BCI system in 2012 ( Long et al, 2012 ). In 2015, Xu Baoguo and others from Southeast University developed an online robot control system based on MI-BCI ( Xu et al, 2015 ). On this platform, the feature extraction and classification of the EEG signal of hand movement imagination are realized, and the average classification accuracy rate reaches 91.5%.…”