Brain-computer interface with language model-electroencephalography fusion for locked-in syndrome.
Brain-computer interface with language model-electroencephalography fusion for locked-in syndrome.
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DOI：
10.1177/1545968313516867
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发表时间：
2014-05
影响因子：
4.2
通讯作者：
Fried-Oken MB
中科院分区：
文献类型：
--
作者：
Oken BS;Orhan U;Roark B;Erdogmus D;Fowler A;Mooney A;Peters B;Miller M;Fried-Oken MB
一些非侵入性脑机接口（BCI）系统目前可用于闭锁综合征（LIS），但没有一个在文本生成过程中纳入统计语言模型。开始使用非侵入性BCI解决LIS患者的通信需求，该BCI涉及符号的快速串行视觉呈现（RSVP）和具有EEG和语言模型融合的独特分类器。RSVP Keyboard™具有多项独特功能。单个字母以每秒2.5个的速度显示。基于EEG的字母作为目标或非目标的计算机分类是使用机器学习来执行的，该机器学习通过贝叶斯融合结合了用于字母预测的语言模型，使得目标仅被呈现1-4次。入组了9名LIS受试者和9名健康对照者。筛选后，受试者首先校准系统，然后完成一系列平衡的单词生成掌握任务，这些任务设计有五个递增的难度级别，通过选择语言模型效用自然下降的短语来增加难度。六名LIS参与者和九名对照完成了实验。所有参加地雷影响调查的人都成功地掌握了一级的拼写，一个科目达到了五级。9名对照组参与者中有6名达到了5级。具有不完整LIS的个人可以从基于EEG的BCI系统中受益，该系统依赖于EEG分类和统计语言模型。讨论了进一步改进该系统的步骤。
Some non-invasive brain computer interface (BCI) systems are currently available for locked-in syndrome (LIS) but none have incorporated a statistical language model during text generation. To begin to address the communication needs of individuals with LIS using a non-invasive BCI that involves Rapid Serial Visual Presentation (RSVP) of symbols and a unique classifier with EEG and language model fusion. The RSVP Keyboard™ was developed with several unique features. Individual letters are presented at 2.5 per sec. Computer classification of letters as targets or non-targets based on EEG is performed using machine learning that incorporates a language model for letter prediction via Bayesian fusion enabling targets to be presented only 1–4 times. Nine participants with LIS and nine healthy controls were enrolled. After screening, subjects first calibrated the system, and then completed a series of balanced word generation mastery tasks that were designed with five incremental levels of difficulty, that increased by selecting phrases for which the utility of the language model decreased naturally. Six participants with LIS and nine controls completed the experiment. All LIS participants successfully mastered spelling at level one and one subject achieved level five. Six of nine control participants achieved level five. Individuals who have incomplete LIS may benefit from an EEG-based BCI system, which relies on EEG classification and a statistical language model. Steps to further improve the system are discussed.
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影响因子：
1.8
作者：
Fager, Susan;Beukelman, David R.;Baker, John
通讯作者：
Baker, John
影响因子：
4.6
作者：
Schalk, G;McFarland, DJ;Wolpaw, JR
通讯作者：
Wolpaw, JR
影响因子：
4
作者：
Brunner P;Joshi S;Briskin S;Wolpaw JR;Bischof H;Schalk G
通讯作者：
Schalk G
影响因子：
3.7
作者：
Jung, TP;Makeig, S;Sejnowski, TJ
通讯作者：
Sejnowski, TJ
DOI：
10.1109/icassp.2012.6287966
发表时间：
2012
期刊：
Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)
影响因子：
--
作者：
Orhan U;Hild KE 2nd;Erdogmus D;Roark B;Oken B;Fried-Oken M
通讯作者：
Fried-Oken M