Suppr超能文献

上肢运动持续时间的皮质脑电图编码

Electrocorticogram encoding of upper extremity movement duration.

作者信息

Wang Po T, King Christine E, McCrimmon Colin M, Shaw Susan J, Millett David E, Liu Charles Y, Chui Luis A, Nenadic Zoran, Do An H

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:1243-6. doi: 10.1109/EMBC.2014.6943822.

Abstract

Electrocorticogram (ECoG) is a promising long-term signal acquisition platform for brain-computer interface (BCI) systems such as upper extremity prostheses. Several studies have demonstrated decoding of arm and finger trajectories from ECoG high-gamma band (80-160 Hz) signals. In this study, we systematically vary the velocity of three elementary movement types (pincer grasp, elbow and shoulder flexion/extension) to test whether the high-gamma band encodes for the entirety of the movements, or merely the movement onset. To this end, linear regression models were created for the durations and amplitudes of high-gamma power bursts and velocity deflections. One subject with 8×8 high-density ECoG grid (4 mm center-to-center electrode spacing) participated in the experiment. The results of the regression models indicated that the power burst durations varied directly with the movement durations (e.g. R(2)=0.71 and slope=1.0 s/s for elbow). The persistence of power bursts for the duration of the movement suggests that the primary motor cortex (M1) is likely active for the entire duration of a movement, instead of providing a marker for the movement onset. On the other hand, the amplitudes were less co-varied. Furthermore, the electrodes of maximum R(2) conformed to somatotopic arrangement of the brain. Also, electrodes responsible for flexion and extension movements could be resolved on the high-density grid. In summary, these findings suggest that M1 may be directly responsible for activating the individual muscle motor units, and future BCI may be able to utilize them for better control of prostheses.

摘要

皮质脑电图(ECoG)是一种很有前景的长期信号采集平台,适用于诸如上肢假肢等脑机接口(BCI)系统。多项研究已证明可从ECoG高伽马波段(80 - 160赫兹)信号中解码手臂和手指的运动轨迹。在本研究中,我们系统地改变三种基本运动类型(捏取、肘部和肩部屈伸)的速度,以测试高伽马波段是对整个运动进行编码,还是仅仅对运动起始进行编码。为此,针对高伽马功率爆发的持续时间和幅度以及速度偏差建立了线性回归模型。一名佩戴8×8高密度ECoG网格(电极中心距为4毫米)的受试者参与了实验。回归模型的结果表明,功率爆发持续时间与运动持续时间直接相关(例如,肘部运动的R² = 0.71,斜率 = 1.0秒/秒)。运动过程中功率爆发的持续性表明,初级运动皮层(M1)在运动的整个持续时间内可能都是活跃的,而不是仅提供运动起始的标记。另一方面,幅度的协变程度较小。此外,R²最大值对应的电极符合大脑的躯体定位排列。而且,在高密度网格上可以分辨出负责屈伸运动的电极。总之,这些发现表明M1可能直接负责激活各个肌肉运动单元,未来的脑机接口或许能够利用它们更好地控制假肢。

相似文献

1
Electrocorticogram encoding of upper extremity movement duration.
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:1243-6. doi: 10.1109/EMBC.2014.6943822.
2
Characterization of electrocorticogram high-gamma signal in response to varying upper extremity movement velocity.
Brain Struct Funct. 2017 Nov;222(8):3705-3748. doi: 10.1007/s00429-017-1429-8. Epub 2017 May 18.
3
Comparison of decoding resolution of standard and high-density electrocorticogram electrodes.
J Neural Eng. 2016 Apr;13(2):026016. doi: 10.1088/1741-2560/13/2/026016. Epub 2016 Feb 9.
4
Decoding three-dimensional reaching movements using electrocorticographic signals in humans.
J Neural Eng. 2016 Apr;13(2):026021. doi: 10.1088/1741-2560/13/2/026021. Epub 2016 Feb 23.
5
Reconstruction of reaching movement trajectories using electrocorticographic signals in humans.
PLoS One. 2017 Sep 20;12(9):e0182542. doi: 10.1371/journal.pone.0182542. eCollection 2017.
8
Sensitivity and specificity of upper extremity movements decoded from electrocorticogram.
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:5618-21. doi: 10.1109/EMBC.2013.6610824.
9
Decoding natural grasp types from human ECoG.
Neuroimage. 2012 Jan 2;59(1):248-60. doi: 10.1016/j.neuroimage.2011.06.084. Epub 2011 Jul 8.
10
Decoding individual finger movements from one hand using human EEG signals.
PLoS One. 2014 Jan 8;9(1):e85192. doi: 10.1371/journal.pone.0085192. eCollection 2014.

引用本文的文献

1
Decoding Movement From Electrocorticographic Activity: A Review.
Front Neuroinform. 2019 Dec 3;13:74. doi: 10.3389/fninf.2019.00074. eCollection 2019.
2
Spatial-Temporal Dynamics of the Sensorimotor Cortex: Sustained and Transient Activity.
IEEE Trans Neural Syst Rehabil Eng. 2018 May;26(5):1084-1092. doi: 10.1109/TNSRE.2018.2821058.
3
Characterization of electrocorticogram high-gamma signal in response to varying upper extremity movement velocity.
Brain Struct Funct. 2017 Nov;222(8):3705-3748. doi: 10.1007/s00429-017-1429-8. Epub 2017 May 18.
4
Comparison of decoding resolution of standard and high-density electrocorticogram electrodes.
J Neural Eng. 2016 Apr;13(2):026016. doi: 10.1088/1741-2560/13/2/026016. Epub 2016 Feb 9.

本文引用的文献

1
Sensitivity and specificity of upper extremity movements decoded from electrocorticogram.
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:5618-21. doi: 10.1109/EMBC.2013.6610824.
2
An electrocorticographic brain interface in an individual with tetraplegia.
PLoS One. 2013;8(2):e55344. doi: 10.1371/journal.pone.0055344. Epub 2013 Feb 6.
3
Electrocorticographic control of a prosthetic arm in paralyzed patients.
Ann Neurol. 2012 Mar;71(3):353-61. doi: 10.1002/ana.22613. Epub 2011 Nov 2.
4
A durable, low-cost electrogoniometer for dynamic measurement of joint trajectories.
Med Eng Phys. 2011 Jun;33(5):546-52. doi: 10.1016/j.medengphy.2010.12.008. Epub 2011 Jan 17.
5
Electrocorticographic amplitude predicts finger positions during slow grasping motions of the hand.
J Neural Eng. 2010 Aug;7(4):046002. doi: 10.1088/1741-2560/7/4/046002. Epub 2010 May 20.
6
Decoding flexion of individual fingers using electrocorticographic signals in humans.
J Neural Eng. 2009 Dec;6(6):066001. doi: 10.1088/1741-2560/6/6/066001. Epub 2009 Oct 1.
7
8
Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics.
J Neurosci Methods. 2008 Jan 15;167(1):63-81. doi: 10.1016/j.jneumeth.2007.04.019. Epub 2007 May 5.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验