[论文解读] Recognition of Words from the EEG Laplacian

J. Acácio de Barros, C. G. Carvalhaes+2 more|arXiv (Cornell University)|Apr 26, 2012

EEG and Brain-Computer Interfaces参考文献 12被引用 3

一句话总结

本研究通过减少来自远端神经源的伪影，提出使用脑电信号的EEG拉普拉斯变换来增强词识别。通过球面样条插值和带通滤波处理平均脑电信号，该方法提高了七个听觉词的识别准确率，在受试者间表现出一致的性能，并凸显了神经反应的空间同构性。

ABSTRACT

Recent works on the relationship between the electro-encephalogram (EEG) data and psychological stimuli show that EEG recordings can be used to recognize an auditory stimulus presented to a subject. The recognition rate is, however, strongly affected by technical and physiological artifacts. In this work, subjects were presented seven auditory simuli in the form of English words (first, second, third, left, right, yes, and no), and the time-locked electric field was recorded with a 64 channel Neuroscan EEG system. We used the surface Laplacian operator to eliminate artifacts due to sources located at regions far from the electrode. Our intent with the Laplacian was to improve the recognition rates of auditory stimuli from the electric field. To compute the Laplacian, we used a spline interpolation from spherical harmonics. The EEG Laplacian of the electric field were average over trials for the same auditory stimulus, and with those averages we constructed prototypes and test samples. In addition to the Laplacian, we applied Butterworth bandpass digital filters to the averaged prototypes and test samples, and compared the filtered test samples against the prototypes using a least squares metric in the time domain. We also analyzed the effects of the spline interpolation order and bandpass filter parameters in the recognition rates. Our results show that the use of the Laplacian improves the recognition rates and suggests a spatial isomorphism between both subjects.

研究动机与目标

• 通过减少远端脑源引起的噪声和伪影，提升从EEG信号中识别听觉词的性能。
• 研究表面拉普拉斯算子在提升EEG信号质量以用于认知解码方面的有效性。
• 评估样条插值阶数和滤波参数对识别性能的影响。
• 利用拉普拉斯变换的EEG数据，探索不同受试者间神经反应的空间一致性（同构性）。

提出的方法

• 基于球谐函数，使用球面样条插值对EEG数据应用表面拉普拉斯算子，以抑制体积导抗效应。
• 对七个听觉词（first, second, third, left, right, yes, no）的每种子试验的脑电信号进行平均，形成原型信号。
• 使用巴特沃斯带通数字滤波器对测试样本进行预处理，以隔离相关频带。
• 在时域中使用最小二乘度量，将滤波后的测试样本与原型信号进行比较，以实现分类。
• 系统性地改变样条插值阶数和滤波参数，以评估其对识别准确率的影响。

实验结果

研究问题

• RQ1 与原始EEG信号相比，EEG拉普拉斯变换是否能提高听觉词的识别率？
• RQ2 样条插值阶数的选择如何影响拉普拉斯估计的质量及后续的识别性能？
• RQ3 针对拉普拉斯变换后的EEG数据，提升词识别的最优带通滤波器配置是什么？
• RQ4 在不同受试者之间，对相同词语是否存在一致的空间反应模式（空间同构性）？

主要发现

• 使用EEG拉普拉斯变换显著提高了词识别率，通过减少远端神经源引起的伪影。
• 更高阶的样条插值可获得更优的拉普拉斯估计，并提升识别准确率，最优性能在特定插值水平下实现。
• 巴特沃斯带通滤波通过隔离相关神经振荡，提升了识别性能，峰值表现出现在特定频率范围。
• 识别率在受试者间保持一致，表明对相同听觉刺激的神经反应具有空间同构性。
• 拉普拉斯变换与滤波信号比较的结合，实现了对七个目标词的可靠分类。