A Predictive Speller Controlled by a Brain-Computer Interface Based on Motor Imagery
A Predictive Speller Controlled by a Brain-Computer Interface Based on Motor Imagery
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DOI：
10.1145/2362364.2362368
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发表时间：
2012-10-01
影响因子：
3.7
通讯作者：
Matteucci, Matteo
中科院分区：
文献类型：
--
作者：
D'Albis, Tiziano;Blatt, Rossella;Matteucci, Matteo
患有运动障碍的人沟通的可能性有限，通常需要辅助技术来满足这一主要需求。有希望为受严重运动障碍影响的受试者提供基本沟通能力的方法包括脑机接口（bci），即直接将大脑信号转换为设备命令的系统，绕过任何肌肉或神经中介。迄今为止，使用脑机接口进行有效的口头交流仍然是一个悬而未决的问题，主要是由于该技术可以实现的信息传输速率较低。然而，通过智能用户界面设计和采用自然语言处理（NLP）技术进行文本预测，BCI拼写应用程序的性能可以得到显著提高。本研究的目的是为脑机接口拼写应用程序提供一种结合最先进的脑机接口和自然语言处理技术的方法和用户界面，以最大限度地提高系统的整体通信速率。采用的脑机接口模式是运动意象，即当受试者想象移动身体的某个部位时，他/她对特定的大脑节奏产生修改，这些修改通过脑电图实时检测到，并转化为拼写应用程序的命令。通过最大化整体通信速率，我们的方法是双重的：一方面，我们最大化控制信号的信息传输速率，另一方面，我们优化这些信息用于口头通信的方式。所取得的结果令人满意，并可与文献报道的最新运动意象BCI拼写器相媲美。三个被试的拼写率分别为3 char/min， 2.7 char/min和2 char/min。
Persons suffering from motor disorders have limited possibilities for communicating and normally require assistive technologies to fulfill this primary need. Promising means of providing basic communication abilities to subjects affected by severe motor impairments include brain-computer interfaces (BCIs), that is, systems that directly translate brain signals into device commands, bypassing any muscle or nerve mediation. To date, the use of BCIs for effective verbal communication is yet an open issue, primarily due to the low rates of information transfer that can be achieved with this technology. Still, performance of BCI spelling applications could be considerably improved by a smart user interface design and by the adoption of natural language processing (NLP) techniques for text prediction. The objective of this work is to suggest an approach and a user interface for BCI spelling applications combining state-of-the-art BCI and NLP techniques to maximize the overall communication rate of the system. The BCI paradigm adopted is motor imagery, that is, when the subject imagines moving a certain part of the body, he/she produces modifications to specific brain rhythms that are detected in real-time through an electroencephalogram and translated into commands for a spelling application. By maximizing the overall communication rate, our approach is twofold: on one hand, we maximize the information transfer rate from the control signal, on the other hand, we optimize the way this information is employed for the purpose of verbal communication. The achieved results are satisfactory and comparable with the latest works reported in literature on motor-imagery BCI spellers. For the three subjects tested, we obtained a spelling rate of respectively 3 char/min, 2.7 char/min, and 2 char/min.