Classification methods for ongoing EEG and MEG signals
Classification methods for ongoing EEG and MEG signals
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DOI：
10.4067/s0716-97602007000500005
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发表时间：
2007-01-01
影响因子：
6.7
通讯作者：
GARNERO, LINE
中科院分区：
文献类型：
--
作者：
BESSERVE, MICHEL;JERBI, KARIM;GARNERO, LINE
分类算法通过从受试者大脑活动的高度多元非侵入性记录中提取有用信息来帮助预测受试者精神状态的定性属性。特别是，将它们应用于脑磁图（MEG）和脑电图（EEG）是一项具有挑战性和前景的任务，具有突出的实际应用，例如脑机接口（BCI）。在本文中，我们首先回顾了主要的分类技术的原理，并讨论其应用于MEG和EEG数据分类。接下来，我们调查的行为分类方法使用真实的数据记录在MEG可视化实验。特别是，我们研究的分类算法的影响，在这个分类器中使用的定量功能变量，和验证方法。此外，我们的研究结果表明，通过调查分类器系数的分布，有可能推断知识，并构建所执行任务的潜在神经机制的功能解释。最后，这里报告的有希望的结果（高达97%的分类准确率在I秒的时间窗口）反映了相当大的潜力，脑磁图的连续分类的精神状态。
Classification algorithms help predict the qualitative properties of a subject's mental state by extracting useful information from the highly multivariate non-invasive recordings of his brain activity. In particular, applying them to Magneto-encephalography (MEG) and electro-encephalography (EEG) is a challenging and promising task with prominent practical applications to e.g. Brain Computer Interface (BCI). In this paper, we first review the principles of the major classification techniques and discuss their application to MEG and EEG data classification. Next, we investigate the behavior of classification methods using real data recorded during a MEG visuomotor experiment. In particular, we study the influence of the classification algorithm, of the quantitative functional variables used in this classifier, and of the validation method. In addition, our findings suggest that by investigating the distribution of classifier coefficients, it is possible to infer knowledge and construct functional interpretations of the underlying neural mechanisms of the performed tasks. Finally, the promising results reported here (up to 97% classification accuracy on I-second time windows) reflect the considerable potential of MEG for the continuous classification of mental states.