Abstract
Abstract Aiming to develop an noninvasive BMI control system with EEG (electroencephalogram) signals to control external devices such as prostheses and robots for rehabilitation and/or power support, four different tasks corresponding to different brain excitation degrees are designed. Their EEG spectra are analyzed with short-time fast Fourier transform (STFFT), and their features of mu and beta rhythms corresponding to the different tasks are extracted. The extracted features are used to control a one-joint robot arm and their corresponding results are compared. The results show that the EEG signal when a subject is holding a weight is comparatively more stable than the EEG signals in other tasks such as motor imagery. This implies an effective way for power assist by EEG signals.
Highlights
Brain-machine interfaces (BMIs) are a technology that allow interaction between humans and artificial devices
The results show that the EEG signal when a subject is holding a weight is comparatively much stabler than the EEG in other cases such as motor imagery
Experimental results The experimental results of subjects A and B are shown in Figures 12 and 13, respectively
Summary
Brain-machine interfaces (BMIs) are a technology that allow interaction between humans and artificial devices. They rely on continuous, real-time interaction between living neuronal tissue and artificial effectors. Prosthetic devices controlled by a human subject with invasive BMI is reported [8]. Though these invasive BMIs have made great success, they face substantial technical difficulties and entail significant clinical risks: they require that recording electrodes be implanted in the cortex and function well for long periods, and they risk infection and other damages to the brain. The efforts to develop them, despite these disadvantages, are based on the widespread belief that only invasive BMIs will be able
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