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采用单个硬脑膜下通道的微创脑-机接口研究:视觉拼写器研究。

Toward a minimally invasive brain-computer interface using a single subdural channel: a visual speller study.

机构信息

Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.

出版信息

Neuroimage. 2013 May 1;71:30-41. doi: 10.1016/j.neuroimage.2012.12.069. Epub 2013 Jan 10.

Abstract

Electrocorticography (ECoG) has attracted increasing interest for implementing advanced brain-computer interfaces (BCIs) in the past decade. However, real-life application of ECoG BCI demands mitigation of its invasive nature by minimizing both the size of the involved brain regions and the number of implanted electrodes. In this study, we employed a recently proposed BCI paradigm that utilizes the attentional modulation of visual motion response. With ECoG data collected from five epilepsy patients, power increase of the high gamma (60-140Hz) frequency range was found to be associated with the overtly attended moving visual stimuli in the parietal-temporal-occipital junction and the occipital cortex. Event-related potentials (ERPs) were elicited as well but with broader cortical distribution. We achieved significantly higher BCI classification accuracy by employing both high gamma and ERP responses from a single ECoG electrode than by using ERP responses only (84.22±5.54% vs. 75.48±4.18%, p<0.005, paired t-test, 3-trial averaging, binary results of attended vs. unattended). More importantly, the high gamma responses were located within brain regions specialized in visual motion processing as mapped by fMRI, suggesting the spatial location for electrode implantation can be determined prior to surgery using non-invasive imaging. Our findings demonstrate the feasibility of implementing a minimally invasive ECoG BCI.

摘要

在过去的十年中,脑电描记术(ECoG)因其在高级脑机接口(BCI)中的应用而引起了越来越多的关注。然而,要将 ECoG BCI 应用于实际生活,就需要通过最小化涉及的脑区大小和植入的电极数量来减轻其侵入性。在这项研究中,我们采用了一种最近提出的 BCI 范式,该范式利用视觉运动反应的注意力调节。通过对五名癫痫患者的 ECoG 数据进行分析,我们发现高伽马(60-140Hz)频段的功率增加与顶颞枕交界处和枕叶皮质的明显注视运动视觉刺激有关。同时也引发了事件相关电位(ERPs),但具有更广泛的皮质分布。我们通过单个 ECoG 电极同时使用高伽马和 ERP 响应,实现了显著更高的 BCI 分类准确性,而仅使用 ERP 响应的准确性则较低(84.22±5.54%比 75.48±4.18%,p<0.005,配对 t 检验,3 次试验平均,关注与不关注的二分结果)。更重要的是,高伽马响应位于 fMRI 映射的专门用于视觉运动处理的脑区,这表明可以使用非侵入性成像技术在手术前确定电极植入的空间位置。我们的研究结果表明,实现微创 ECoG BCI 是可行的。

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