Towards a next-generation hearing aid through brain state classification and modeling.
Towards a next-generation hearing aid through brain state classification and modeling.
复制标题
通过大脑状态分类和建模迈向下一代助听器。
DOI：
10.1109/embc.2013.6610124
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Lee,AdrianKC
中科院分区：
文献类型：
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作者：
Wronkiewicz,Mark;Larson,Eric;Lee,AdrianKC
传统的大脑状态分类主要基于两个众所周知的神经生物标志物：P300和运动想象/事件相关频率调制。目前，许多脑机接口（BCI）系统已经成功地帮助严重神经肌肉残疾患者恢复独立。为了将这种神经工程的成功转化为助听器应用，我们必须能够在以前没有被纳入这些系统的皮层区域中可靠地捕获整个人群的脑电波，例如背外侧前额叶皮层（DLPFC）和右颞顶交界处。在这里，我们提出了一个大脑状态分类框架，它结合了个人的解剖信息，并通过在分类阶段之前应用适当的皮质加权函数来考虑受试者之间潜在的解剖和功能差异。使用逆成像方法，使用模拟EEG数据来表明我们的方法可以优于传统的大脑状态分类方法，该方法仅在单个受试者的数据上训练，而不考虑在群体水平上可用的数据。
Traditional brain-state classifications are primarily based on two well-known neural biomarkers: P300 and motor imagery / event-related frequency modulation. Currently, many brain-computer interface (BCI) systems have successfully helped patients with severe neuromuscular disabilities to regain independence. In order to translate this neural engineering success to hearing aid applications, we must be able to capture brain waves across the population reliably in cortical regions that have not previously been incorporated in these systems before, for example, dorsolateral prefrontal cortex (DLPFC) and right temporoparietal junction. Here, we present a brain-state classification framework that incorporates individual anatomical information and accounts for potential anatomical and functional differences across subjects by applying appropriate cortical weighting functions prior to the classification stage. Using an inverse imaging approach, use simulated EEG data to show that our method can outperform the traditional brain-state classification approach that trains only on individual subject's data without considering data available at a population level.