Department of Biomedical Engineering, Hanyang University, Seoul 133-731, Korea.
J Neural Eng. 2013 Apr;10(2):026021. doi: 10.1088/1741-2560/10/2/026021. Epub 2013 Mar 26.
Some patients suffering from severe neuromuscular diseases have difficulty controlling not only their bodies but also their eyes. Since these patients have difficulty gazing at specific visual stimuli or keeping their eyes open for a long time, they are unable to use the typical steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems. In this study, we introduce a new paradigm for SSVEP-based BCI, which can be potentially suitable for disabled individuals with impaired oculomotor function.
The proposed electroencephalography (EEG)-based BCI system allows users to express their binary intentions without needing to open their eyes. A pair of glasses with two light emitting diodes flickering at different frequencies was used to present visual stimuli to participants with their eyes closed, and we classified the recorded EEG patterns in the online experiments conducted with five healthy participants and one patient with severe amyotrophic lateral sclerosis (ALS).
Through offline experiments performed with 11 participants, we confirmed that human SSVEP could be modulated by visual selective attention to a specific light stimulus penetrating through the eyelids. Furthermore, the recorded EEG patterns could be classified with accuracy high enough for use in a practical BCI system. After customizing the parameters of the proposed SSVEP-based BCI paradigm based on the offline analysis results, binary intentions of five healthy participants were classified in real time. The average information transfer rate of our online experiments reached 10.83 bits min(-1). A preliminary online experiment conducted with an ALS patient showed a classification accuracy of 80%.
The results of our offline and online experiments demonstrated the feasibility of our proposed SSVEP-based BCI paradigm. It is expected that our 'eyes-closed' SSVEP-based BCI system can be potentially used for communication of disabled individuals with impaired oculomotor function.
一些患有严重神经肌肉疾病的患者不仅难以控制身体，还难以控制眼睛。由于这些患者难以注视特定的视觉刺激或长时间睁开眼睛，他们无法使用典型的基于稳态视觉诱发电位（SSVEP）的脑机接口（BCI）系统。在这项研究中，我们引入了一种新的基于 SSVEP 的 BCI 范式，它可能适合患有眼球运动功能障碍的残疾个体。
所提出的基于脑电图（EEG）的 BCI 系统允许用户在无需睁开眼睛的情况下表达他们的二元意图。使用一对带有两个以不同频率闪烁的发光二极管的眼镜向闭着眼睛的参与者呈现视觉刺激，我们对五名健康参与者和一名患有严重肌萎缩性侧索硬化症（ALS）的患者进行的在线实验中记录的 EEG 模式进行了分类。
通过 11 名参与者进行的离线实验，我们证实了人类 SSVEP 可以通过对穿透眼皮的特定光刺激的视觉选择性注意来调制。此外，所记录的 EEG 模式可以以足够高的准确性进行分类，可用于实际的 BCI 系统。根据离线分析结果定制基于 SSVEP 的 BCI 范式的参数后，我们实时分类了五名健康参与者的二元意图。我们的在线实验的平均信息传输率达到了 10.83 位/分钟。对一名 ALS 患者进行的初步在线实验显示出 80%的分类准确性。
离线和在线实验的结果证明了我们提出的基于 SSVEP 的 BCI 范式的可行性。预计我们的“闭眼”基于 SSVEP 的 BCI 系统可用于患有眼球运动功能障碍的残疾个体的交流。
