Limits...
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.


Examples of the actual and predicted values for each muscle. Table 1 contains the leg-end for all abbreviations. The dashed line represents the actual values, and the solid line represents the predicted values.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4264470&req=5

Figure 7: Examples of the actual and predicted values for each muscle. Table 1 contains the leg-end for all abbreviations. The dashed line represents the actual values, and the solid line represents the predicted values.

Mentions: An example of a prediction over 2 s is shown in Figure 7. The solid line represents the actual values, and the dashed line represents the predicted values. In most cases, the trends and peak values were well matched. Although a peak time shift was seen in some cases, such as for the Flexor Carpi Ulnaris (FCU), rise times mostly matched.


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)

Examples of the actual and predicted values for each muscle. Table 1 contains the leg-end for all abbreviations. The dashed line represents the actual values, and the solid line represents the predicted values.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: Examples of the actual and predicted values for each muscle. Table 1 contains the leg-end for all abbreviations. The dashed line represents the actual values, and the solid line represents the predicted values.
Mentions: An example of a prediction over 2 s is shown in Figure 7. The solid line represents the actual values, and the dashed line represents the predicted values. In most cases, the trends and peak values were well matched. Although a peak time shift was seen in some cases, such as for the Flexor Carpi Ulnaris (FCU), rise times mostly matched.

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.