Abstract
Brain–machine interfaces (BMIs) are promising technologies for rehabilitation of upper limb functions in patients with severe paralysis. We previously developed a BMI prosthetic arm for a monkey implanted with electrocorticography (ECoG) electrodes, and trained it in a reaching task. The stability of the BMI prevented incorrect movements due to misclassification of ECoG patterns. As a trade-off for the stability, however, the latency (the time gap between the monkey's actual motion and the prosthetic arm movement) was about 200 ms. Therefore, in this study, we aimed to improve the response time of the BMI prosthetic arm. We focused on the generation of a trigger event by decoding muscle activity in order to predict integrated electromyograms (iEMGs) from the ECoGs. We verified the achievability of our method by conducting a performance test of the proposed method with actual achieved iEMGs instead of predicted iEMGs. Our results confirmed that the proposed method with predicted iEMGs eliminated the time delay. In addition, we found that motor intention is better reflected by muscle activity estimated from brain activity rather than actual muscle activity. Therefore, we propose that using predicted iEMGs to guide prosthetic arm movement results in minimal delay and excellent performance.
Highlights
Brain-machine interfaces (BMIs), which are a type of manmachine interface that provides a direct connection between the brain and external devices, can be divided into 2 types: input-type and output-type
To construct a BMI prosthetic arm that performs a reaching task, it is preferable for the response to use information generated by muscle activity and not just the movement
A trigger event is generated according to the predicted integrated EMGs (iEMGs) from ECoGs by using a partial least squares (PLS) regression
Summary
Brain-machine interfaces (BMIs), which are a type of manmachine interface that provides a direct connection between the brain and external devices, can be divided into 2 types: input-type and output-type. The initial studies on BMI focused on invasive signal detection of brain activity, and they achieved highly successful control of a prosthetic hand (Velliste et al, 2008) with good spatial resolution and signal-to-noise ratios. Degeneration and necrosis limit the long-term use of these invasive signal detection methods (Szarowski et al, 2003; Biran et al, 2005) To overcome this problem, an electrocorticography (ECoG) electrode was developed. This is an invasive signal detection method involving the use of a surface electrode on the cerebral cortex under the dura matter. It has long-term stability with low clinical risk. ECoGs have been used to develop output-type BMI systems for twodimensional cursor control and motion prediction of the upper arm (Schalk et al, 2007; Pistohl et al, 2008; Uejima et al, 2009; Yanagisawa et al, 2009; Chao et al, 2010; Yanagisawa et al, 2011)
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