Early classification of motor tasks using dynamic functional connectivity graphs from EEG
Early classification of motor tasks using dynamic functional connectivity graphs from EEG
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DOI：
10.1088/1741-2552/abce70
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发表时间：
2021-02-01
影响因子：
4
通讯作者：
Najafizadeh，Laleh
中科院分区：
文献类型：
--
作者：
Shamsi，Foroogh;Haddad，Ali;Najafizadeh，Laleh
脑电（EEG）信号的分类是脑机接口（BCI）发展中的一个具有挑战性的问题。本文提出了一种新的特征提取方法脑电记录来解决这个问题。ApproachThe所提出的方法是基于大脑功能的概念，在一个动态的方式，并利用动态功能连接图。EEG数据首先被分割成间隔，在此期间，功能网络维持其连接。然后，对每个识别出的片段的功能连接网络进行本地化，并构建图形，这些图形将用作特征。为了利用所生成的图形的动态性质，长短期记忆分类器用于classification.Main resultsFeatures提取的各种持续时间的刺激后EEG数据与运动执行和图像任务被用来测试分类器的性能。结果表明，平均准确率为85.32%，约只有500 ms后，刺激presentation.SignificanceOur的结果表明，第一次，使用所提出的特征提取方法，它是可能的分类运动任务从EEG记录使用短的时间间隔的数据在数百毫秒（如500 ms）的顺序。这一时间比以前报告的时间短得多。这些结果将对提高BCI的有效性和速度产生重大影响，特别是对于辅助技术中使用的BCI。
ObjectiveClassification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem.ApproachThe proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a long short term memory classifier is employed for classification.Main resultsFeatures extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% about only 500 ms after stimulus presentation.SignificanceOur results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (eg 500 ms). This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.