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在脊髓损伤后,EEG运动节律的功能连接性

Jiancai Leng¹, Xin Yu¹, Chongfeng Wang¹

¹International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), No.3501, Daxue Road Changqing District, Jinan, 250353 Shandong China.

Cognitive neurodynamics

|November 18, 2024

概括

本研究探讨了使用电脑电图 (EEG) 信号在脊髓损伤 (SCI) 患者的大脑网络的变化. 阶段锁定值 (PLV) 方法揭示了改变的连接性,有助于基于运动图像 (MI) 的脑计算机接口 (BCI) 康复.

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
康复科学康复科学康复科学

背景情况:

脊髓损伤 (SCI) 损害运动功能,需要先进的康复策略.
基于运动图像 (MI) 的脑计算机接口 (BCI) 系统为SCI的运动功能重建提供了潜力.
在SCI患者中分析电脑电图 (EEG) 信号是具有挑战性的,因为信号与噪声的比率低,波动性高.

研究的目的:

研究SCI后大脑网络连接和功能机制的变化.
评估阶段锁定值 (PLV) 方法用于分析SCI患者的大脑网络的有效性.
评估基于PLV的新型特征提取方法 (PLV-CSP) 在SCI中基于MI的BCI的性能.

主要方法:

在SCI患者和健康对照者 (HC) 的α和β节奏中使用相锁定值 (PLV) 构建的大脑网络.
分析了SCI和HC组之间大脑网络连接强度的差异.
从SCI患者的MI数据中提取了具有共同空间模式 (PLV-CSP) 的PLV特征.

主要成果:

与HC组相比,SCI患者表现出较弱的α节律连接强度.
与HC组相比,SCI患者显示出更强的β节律连接强度.
在12名SCI患者中,PLV-CSP功能通过十倍交叉验证实现了100%的最高分类精度和95.6%的平均精度.

结论:

该PLV方法有效地分析SCI患者的大脑网络变化.
拟议的PLV-CSP方法在SCI康复中证明了基于MI的BCI的高准确性.
这种方法对SCI病理学研究和开发有效的BCI康复系统充满希望.

关键词:

大脑网络大脑网络运动图像中的运动图像.模式识别模式识别模式识别阶段锁定值的阶段锁定值.脊髓损伤导致的脊髓损伤

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