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使用稳态视觉诱发电位研究用于脑机接口的感觉能力、特征匹配和基于评估的适应性。

Examining sensory ability, feature matching and assessment-based adaptation for a brain-computer interface using the steady-state visually evoked potential.

作者信息

Brumberg Jonathan S, Nguyen Anh, Pitt Kevin M, Lorenz Sean D

机构信息

a Department of Speech-Language-Hearing: Sciences & Disorders , University of Kansas , Lawrence , KS , USA.

b Department of Speech Language & Hearing Sciences , College of Health & Rehabilitation Sciences: Sargent College, Boston University , Boston , MA , USA.

出版信息

Disabil Rehabil Assist Technol. 2019 Apr;14(3):241-249. doi: 10.1080/17483107.2018.1428369. Epub 2018 Jan 31.

Abstract

PURPOSE

We investigated how overt visual attention and oculomotor control influence successful use of a visual feedback brain-computer interface (BCI) for accessing augmentative and alternative communication (AAC) devices in a heterogeneous population of individuals with profound neuromotor impairments. BCIs are often tested within a single patient population limiting generalization of results. This study focuses on examining individual sensory abilities with an eye toward possible interface adaptations to improve device performance.

METHODS

Five individuals with a range of neuromotor disorders participated in four-choice BCI control task involving the steady state visually evoked potential. The BCI graphical interface was designed to simulate a commercial AAC device to examine whether an integrated device could be used successfully by individuals with neuromotor impairment.

RESULTS

All participants were able to interact with the BCI and highest performance was found for participants able to employ an overt visual attention strategy. For participants with visual deficits to due to impaired oculomotor control, effective performance increased after accounting for mismatches between the graphical layout and participant visual capabilities.

CONCLUSION

As BCIs are translated from research environments to clinical applications, the assessment of BCI-related skills will help facilitate proper device selection and provide individuals who use BCI the greatest likelihood of immediate and long term communicative success. Overall, our results indicate that adaptations can be an effective strategy to reduce barriers and increase access to BCI technology. These efforts should be directed by comprehensive assessments for matching individuals to the most appropriate device to support their complex communication needs. Implications for Rehabilitation Brain computer interfaces using the steady state visually evoked potential can be integrated with an augmentative and alternative communication device to provide access to language and literacy for individuals with neuromotor impairment. Comprehensive assessments are needed to fully understand the sensory, motor, and cognitive abilities of individuals who may use brain-computer interfaces for proper feature matching as selection of the most appropriate device including optimization device layouts and control paradigms. Oculomotor impairments negatively impact brain-computer interfaces that use the steady state visually evoked potential, but modifications to place interface stimuli and communication items in the intact visual field can improve successful outcomes.

摘要

目的

我们研究了明显的视觉注意力和眼动控制如何影响视觉反馈脑机接口(BCI)在患有严重神经运动障碍的异质人群中成功用于辅助和替代沟通(AAC)设备。BCI通常在单一患者群体中进行测试,限制了结果的普遍性。本研究着重于检查个体的感觉能力,以期对接口进行可能的调整以提高设备性能。

方法

五名患有一系列神经运动障碍的个体参与了涉及稳态视觉诱发电位的四选BCI控制任务。BCI图形界面设计用于模拟商业AAC设备,以检查神经运动障碍个体是否能够成功使用集成设备。

结果

所有参与者都能够与BCI进行交互,并且发现能够采用明显视觉注意力策略的参与者表现最佳。对于因眼动控制受损而存在视觉缺陷的参与者,在考虑图形布局与参与者视觉能力之间的不匹配后,有效表现有所提高。

结论

随着BCI从研究环境转化为临床应用,对BCI相关技能的评估将有助于促进正确的设备选择,并为使用BCI的个体提供即时和长期沟通成功的最大可能性。总体而言,我们的结果表明,调整可以是减少障碍并增加对BCI技术访问的有效策略。这些努力应以全面评估为指导,以使个体与最合适的设备相匹配,以满足他们复杂的沟通需求。康复的意义 使用稳态视觉诱发电位的脑机接口可以与辅助和替代沟通设备集成,为神经运动障碍个体提供语言和读写能力。需要进行全面评估,以充分了解可能使用脑机接口的个体的感觉、运动和认知能力,以便进行适当的特征匹配,从而选择最合适的设备,包括优化设备布局和控制范式。眼动障碍会对使用稳态视觉诱发电位的脑机接口产生负面影响,但将接口刺激和沟通项目放置在完整视野中的修改可以改善成功结果。

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Guidelines for Feature Matching Assessment of Brain-Computer Interfaces for Augmentative and Alternative Communication.
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2
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Am J Speech Lang Pathol. 2018 Feb 6;27(1):1-12. doi: 10.1044/2017_AJSLP-16-0244.
3
SOLICITING BCI USER EXPERIENCE FEEDBACK FROM PEOPLE WITH SEVERE SPEECH AND PHYSICAL IMPAIRMENTS.
Brain Comput Interfaces (Abingdon). 2016 Jan 1;3(1):47-58. doi: 10.1080/2326263x.2015.1138056. Epub 2016 Feb 3.
4
Performance predictors of brain-computer interfaces in patients with amyotrophic lateral sclerosis.
J Neural Eng. 2016 Apr;13(2):026002. doi: 10.1088/1741-2560/13/2/026002. Epub 2016 Jan 29.
5
The effects of working memory on brain-computer interface performance.
Clin Neurophysiol. 2016 Feb;127(2):1331-1341. doi: 10.1016/j.clinph.2015.10.038. Epub 2015 Oct 23.
6
Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface.
J Neural Eng. 2015 Aug;12(4):046008. doi: 10.1088/1741-2560/12/4/046008. Epub 2015 Jun 2.
7
Building Evidence-based Practice in AAC Display Design for Young Children: Current Practices and Future Directions.
Augment Altern Commun. 2015 Jun;31(2):124-36. doi: 10.3109/07434618.2015.1035798. Epub 2015 Apr 18.
8
Critical issues using brain-computer interfaces for augmentative and alternative communication.
Arch Phys Med Rehabil. 2015 Mar;96(3 Suppl):S8-15. doi: 10.1016/j.apmr.2014.01.034.
9
Toward independent home use of brain-computer interfaces: a decision algorithm for selection of potential end-users.
Arch Phys Med Rehabil. 2015 Mar;96(3 Suppl):S27-32. doi: 10.1016/j.apmr.2014.03.036.
10
Performance variation in motor imagery brain-computer interface: a brief review.
J Neurosci Methods. 2015 Mar 30;243:103-10. doi: 10.1016/j.jneumeth.2015.01.033. Epub 2015 Feb 8.

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