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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Recognizing Pain in Motor Imagery EEG Recordings Using Dynamic Functional Connectivity Graphs
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Recognizing Pain in Motor Imagery EEG Recordings Using Dynamic Functional Connectivity Graphs

机译：使用动态功能连接图识别运动图像脑电图记录中的疼痛

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The goal of this paper is to investigate whether motor imagery tasks, performed under pain-free versus pain conditions, can be discriminated from electroencephalography (EEG) recordings. Four motor imagery classes of right hand, left hand, foot, and tongue are considered. A functional connectivity-based feature extraction approach along with a long short-term memory (LSTM) classifier are employed for classifying pain-free versus under-pain classes. Moreover, classification is performed in different frequency bands to study the significance of each band in differentiating motor imagery data associated with pain-free and under-pain states. When considering all frequency bands, the average classification accuracy is in the range of 77:86−80:04%. Our frequency-specific analysis shows that the gamma band results in a notably higher accuracy than other bands, indicating the importance of this band in discriminating paino-pain conditions during the execution of motor imagery tasks. In contrast, functional connectivity graphs extracted from delta and theta bands do not seem to provide discriminatory information between pain-free and under-pain conditions. This is the first study demonstrating that motor imagery tasks executed under pain and without pain conditions can be discriminated from EEG recordings. Our findings can provide new insights for developing effective brain computer interface-based assistive technologies for patients who are in real need of them.
机译：本文的目的是研究是否可以从脑电图（EEG）记录中区分在无痛与疼痛条件下执行的运动成像任务。考虑了右手，左手，脚和舌头的四个运动图像类。基于功能连通性的特征提取方法以及长短期记忆（LSTM）分类器用于对无痛与疼痛不足类别进行分类。此外，在不同频带中进行分类以研究每个频带在区分与无痛和疼痛不足状态相关的运动图像数据中的重要性。当考虑所有频带时，平均分类精度在77：86-80：04％的范围内。我们对特定频率的分析表明，γ波段比其他波段具有更高的准确度，这表明该波段对于区分运动图像任务执行过程中的疼痛/无痛状况非常重要。相反，从δ和θ带提取的功能连接图似乎无法提供无痛和疼痛不足情况之间的区分性信息。这是第一项研究，证明可以从EEG记录中区分在疼痛和无疼痛条件下执行的运动成像任务。我们的发现可以为真正有需要的患者开发有效的基于脑计算机接口的辅助技术提供新的见解。

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