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截瘫患者中枢神经性疼痛对基于运动想象的脑机接口性能的影响。

The influence of central neuropathic pain in paraplegic patients on performance of a motor imagery based Brain Computer Interface.

作者信息

Vuckovic A, Hasan M A, Osuagwu B, Fraser M, Allan D B, Conway B A, Nasseroleslami B

机构信息

Biomedical Engineering Division, University of Glasgow, Glasgow, UK.

Biomedical Engineering Division, University of Glasgow, Glasgow, UK; Department of Biomedical Engineering, NED University of Engineering and Technology, Karachi, Pakistan.

出版信息

Clin Neurophysiol. 2015 Nov;126(11):2170-80. doi: 10.1016/j.clinph.2014.12.033. Epub 2015 Jan 30.

DOI:10.1016/j.clinph.2014.12.033
PMID:25698307
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4634336/
Abstract

OBJECTIVE

The aim of this study was to test how the presence of central neuropathic pain (CNP) influences the performance of a motor imagery based Brain Computer Interface (BCI).

METHODS

In this electroencephalography (EEG) based study, we tested BCI classification accuracy and analysed event related desynchronisation (ERD) in 3 groups of volunteers during imagined movements of their arms and legs. The groups comprised of nine able-bodied people, ten paraplegic patients with CNP (lower abdomen and legs) and nine paraplegic patients without CNP. We tested two types of classifiers: a 3 channel bipolar montage and classifiers based on common spatial patterns (CSPs), with varying number of channels and CSPs.

RESULTS

Paraplegic patients with CNP achieved higher classification accuracy and had stronger ERD than paraplegic patients with no pain for all classifier configurations. Highest 2-class classification accuracy was achieved for CSP classifier covering wider cortical area: 82±7% for patients with CNP, 82±4% for able-bodied and 78±5% for patients with no pain.

CONCLUSION

Presence of CNP improves BCI classification accuracy due to stronger and more distinct ERD.

SIGNIFICANCE

Results of the study show that CNP is an important confounding factor influencing the performance of motor imagery based BCI based on ERD.

摘要

目的

本研究旨在测试中枢神经性疼痛（CNP）的存在如何影响基于运动想象的脑机接口（BCI）的性能。

方法

在这项基于脑电图（EEG）的研究中，我们测试了3组志愿者在想象手臂和腿部运动时的BCI分类准确率，并分析了事件相关去同步化（ERD）。这3组包括9名身体健全的人、10名患有CNP（下腹部和腿部）的截瘫患者以及9名没有CNP的截瘫患者。我们测试了两种类型的分类器：一种是3通道双极导联，另一种是基于共同空间模式（CSP）的分类器，其通道数和CSP数量各不相同。

结果

对于所有分类器配置，患有CNP的截瘫患者比无疼痛的截瘫患者实现了更高的分类准确率，并且具有更强的ERD。覆盖更广泛皮质区域的CSP分类器实现了最高的二类分类准确率：患有CNP的患者为82±7%，身体健全的人为82±4%，无疼痛的患者为78±5%。

结论

由于更强且更明显的ERD，CNP的存在提高了BCI分类准确率。

意义

研究结果表明，CNP是基于ERD影响基于运动想象的BCI性能的一个重要混杂因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/68d24a0a4907/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/cd0ef71f3cd3/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/6ff9c4234964/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/497d5076915a/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/1f74e169d3e5/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/20a9542d16c8/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/a2e8578676a3/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/68d24a0a4907/gr7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/cd0ef71f3cd3/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/6ff9c4234964/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/497d5076915a/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/1f74e169d3e5/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/20a9542d16c8/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/a2e8578676a3/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e079/4634336/68d24a0a4907/gr7.jpg

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