Department of Computer Science, City University of Hong Kong, Hong Kong.
Institute for Neural Computation, University of California San Diego, La Jolla, California, United States of America ; Computational Neurobiology Laboratory, Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, United States of America.
PLoS One. 2014 Feb 21;9(2):e88915. doi: 10.1371/journal.pone.0088915. eCollection 2014.
Brain computer interfaces (BCIs) offer a broad class of neurologically impaired individuals an alternative means to interact with the environment. Many BCIs are "synchronous" systems, in which the system sets the timing of the interaction and tries to infer what control command the subject is issuing at each prompting. In contrast, in "asynchronous" BCIs subjects pace the interaction and the system must determine when the subject's control command occurs. In this paper we propose a new idea for BCI which draws upon the strengths of both approaches. The subjects are externally paced and the BCI is able to determine when control commands are issued by decoding the subject's intention for initiating control in dedicated time slots. A single task with randomly interleaved trials was designed to test whether it can be used as stimulus for inducing initiation and non-initiation states when the sensory and motor requirements for the two types of trials are very nearly identical. Further, the essential problem on the discrimination between initiation state and non-initiation state was studied. We tested the ability of EEG spectral power to distinguish between these two states. Among the four standard EEG frequency bands, beta band power recorded over parietal-occipital cortices provided the best performance, achieving an average accuracy of 86% for the correct classification of initiation and non-initiation states. Moreover, delta band power recorded over parietal and motor areas yielded a good performance and thus could also be used as an alternative feature to discriminate these two mental states. The results demonstrate the viability of our proposed idea for a BCI design based on conventional EEG features. Our proposal offers the potential to mitigate the signal detection challenges of fully asynchronous BCIs, while providing greater flexibility to the subject than traditional synchronous BCIs.
脑机接口 (BCI) 为许多神经系统受损的人提供了一种与环境交互的替代方法。许多 BCI 是“同步”系统，其中系统设置交互的时间，并尝试推断受试者在每个提示时发出的控制命令。相比之下，在“异步”BCI 中，受试者控制交互的节奏，系统必须确定何时发生受试者的控制命令。在本文中，我们提出了一种新的 BCI 理念，该理念借鉴了这两种方法的优势。受试者由外部计时，BCI 能够通过解码受试者在专用时隙中启动控制的意图来确定何时发出控制命令。设计了一个具有随机交错试验的单一任务，以测试当两种类型的试验的感觉和运动要求非常相似时，它是否可以用作诱导启动和非启动状态的刺激。此外，研究了区分启动状态和非启动状态的基本问题。我们测试了 EEG 频谱功率区分这两种状态的能力。在四个标准 EEG 频带中，记录在顶枕叶皮层上的β频带功率提供了最佳性能，对启动和非启动状态的正确分类平均准确率达到 86%。此外，记录在顶叶和运动区域的 delta 频带功率也取得了良好的性能，因此也可以用作区分这两种心理状态的替代特征。结果表明，基于传统 EEG 特征的 BCI 设计的我们提出的理念具有可行性。我们的建议提供了减轻完全异步 BCI 的信号检测挑战的潜力，同时为受试者提供了比传统同步 BCI 更大的灵活性。
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