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基于 EEG-EOG 信号的具有视觉反馈的高性能拼写系统。

A High Performance Spelling System based on EEG-EOG Signals With Visual Feedback.

出版信息

IEEE Trans Neural Syst Rehabil Eng. 2018 Jul;26(7):1443-1459. doi: 10.1109/TNSRE.2018.2839116.

Abstract

In this paper, we propose a highly accurate and fast spelling system that employs multi-modal electroencephalography-electrooculography (EEG-EOG) signals and visual feedback technology. Over the last 20 years, various types of speller systems have been developed in brain-computer interface and EOG/eye-tracking research; however, these conventional systems have a tradeoff between the spelling accuracy (or decoding) and typing speed. Healthy users and physically challenged participants, in particular, may become exhausted quickly; thus, there is a need for a speller system with fast typing speed while retaining a high level of spelling accuracy. In this paper, we propose the first hybrid speller system that combines EEG and EOG signals with visual feedback technology so that the user and the speller system can act cooperatively for optimal decision-making. The proposed spelling system consists of a classic row-column event-related potential (ERP) speller, an EOG command detector, and visual feedback modules. First, the online ERP speller calculates classification probabilities for all candidate characters from the EEG epochs. Second, characters are sorted by their probability, and the characters with the highest probabilities are highlighted as visual feedback within the row-column spelling layout. Finally, the user can actively select the character as the target by generating an EOG command. The proposed system shows 97.6% spelling accuracy and an information transfer rate of 39.6 (±13.2) [bits/min] across 20 participants. In our extended experiment, we redesigned the visual feedback and minimized the number of channels (four channels) in order to enhance the speller performance and increase usability. Most importantly, a new weighted strategy resulted in 100% accuracy and a 57.8 (±23.6) [bits/min] information transfer rate across six participants. This paper demonstrates that the proposed system can provide a reliable communication channel for practical speller applications and may be used to supplement existing systems.

摘要

在本文中,我们提出了一种高度准确和快速的拼写系统,该系统采用多模态脑电图-眼电图(EEG-EOG)信号和视觉反馈技术。在过去的 20 年中,脑机接口和 EOG/眼动追踪研究中已经开发出了各种类型的拼写系统;然而,这些传统系统在拼写准确性(或解码)和打字速度之间存在权衡。健康用户和身体有障碍的参与者尤其可能很快感到疲惫;因此,需要一种打字速度快且拼写准确性高的拼写系统。在本文中,我们提出了第一个混合拼写系统,该系统将 EEG 和 EOG 信号与视觉反馈技术相结合,以便用户和拼写系统可以协作以进行最佳决策。所提出的拼写系统由经典的行-列事件相关电位(ERP)拼写器、EOG 命令检测器和视觉反馈模块组成。首先,在线 ERP 拼写器从 EEG 时段计算所有候选字符的分类概率。其次,根据概率对字符进行排序,并在行-列拼写布局中突出显示具有最高概率的字符作为视觉反馈。最后,用户可以通过生成 EOG 命令主动选择目标字符。该系统在 20 名参与者中显示了 97.6%的拼写准确率和 39.6(±13.2)[bits/min]的信息传输率。在我们的扩展实验中,我们重新设计了视觉反馈并减少了通道数量(四个通道),以提高拼写性能并增加可用性。最重要的是,新的加权策略在六名参与者中实现了 100%的准确率和 57.8(±23.6)[bits/min]的信息传输率。本文证明,所提出的系统可为实际拼写应用提供可靠的通信渠道,并可用于补充现有系统。

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