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Brain-machine interface to control a prosthetic arm with monkey ECoGs during periodic movements.

Morishita S, Sato K, Watanabe H, Nishimura Y, Isa T, Kato R, Nakamura T, Yokoi H - Front Neurosci (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Brain Science Inspired Life Support Research Center, The University of Electro-Communications Chofu, Japan.

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.

No MeSH data available.


Difference between the actual start time of the subject and the determined start time of the prosthetic arm. (A) The result of the movement decision method that used accumulated discrimination results. (B) The result of the trigger generation with the predicted iEMG. (C) The results obtained with the actual iEMGs.
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Figure 9: Difference between the actual start time of the subject and the determined start time of the prosthetic arm. (A) The result of the movement decision method that used accumulated discrimination results. (B) The result of the trigger generation with the predicted iEMG. (C) The results obtained with the actual iEMGs.

Mentions: We confirmed the performance of the proposed movement decision method. To detect the start time of the upper arm movement, the deltoid posterior was selected because it increased monotonically with the upper arm movement. Figure 9 shows the difference between the actual start time of the subject and the start times that were determined with each method. In this section, the movement decision method that used accumulated discrimination results is treated as the conventional method (Figure 9A). Moreover, the result of the trigger generation with the predicted iEMG is shown as the proposed method (Figure 9B). The results obtained with the actual iEMGs are shown for comparison (Figure 9C).


Brain-machine interface to control a prosthetic arm with monkey ECoGs during periodic movements.

Morishita S, Sato K, Watanabe H, Nishimura Y, Isa T, Kato R, Nakamura T, Yokoi H - Front Neurosci (2014)

Difference between the actual start time of the subject and the determined start time of the prosthetic arm. (A) The result of the movement decision method that used accumulated discrimination results. (B) The result of the trigger generation with the predicted iEMG. (C) The results obtained with the actual iEMGs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4264470&req=5

Figure 9: Difference between the actual start time of the subject and the determined start time of the prosthetic arm. (A) The result of the movement decision method that used accumulated discrimination results. (B) The result of the trigger generation with the predicted iEMG. (C) The results obtained with the actual iEMGs.
Mentions: We confirmed the performance of the proposed movement decision method. To detect the start time of the upper arm movement, the deltoid posterior was selected because it increased monotonically with the upper arm movement. Figure 9 shows the difference between the actual start time of the subject and the start times that were determined with each method. In this section, the movement decision method that used accumulated discrimination results is treated as the conventional method (Figure 9A). Moreover, the result of the trigger generation with the predicted iEMG is shown as the proposed method (Figure 9B). The results obtained with the actual iEMGs are shown for comparison (Figure 9C).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Brain Science Inspired Life Support Research Center, The University of Electro-Communications Chofu, Japan.

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.

No MeSH data available.