Background

Accurate source localization from electroencephalography (EEG) requires electrode co-registration to brain anatomy, a process that depends on precise measurement of 3D scalp locations. Stylus digitizers and camera-based scanners for such measurements require the subject to remain still and therefore are not ideal for young children or those with movement disorders.

New method

Motion capture accurately measures electrode position in one frame but marker placement adds significant setup time, particularly in high-density EEG. We developed an algorithm, named MoLo and implemented as an open-source MATLAB toolbox, to compute 3D electrode coordinates from a subset of positions measured in motion capture using spline interpolation. Algorithm accuracy was evaluated across 5 different-sized head models.

Results

MoLo interpolation reduced setup time by approximately 10 min for 64-channel EEG. Mean electrode interpolation error was 2.95 ± 1.3 mm (range: 0.38–7.98 mm). Source localization errors with interpolated compared to true electrode locations were below 1 mm and 0.1 mm in 75 % and 35 % of dipoles, respectively.

Comparison with existing methods

MoLo location accuracy is comparable to stylus digitizers and camera-scanners, common in clinical research. The MoLo algorithm could be deployed with other tools beyond motion capture, e.g., a stylus, to extract high-density EEG electrode locations from a subset of measured positions. The algorithm is particularly useful for research involving young children and others who cannot remain still for extended time periods.

Conclusions

Electrode position and source localization errors with MoLo are similar to other modalities supporting its use to measure high-density EEG electrode positions in research and clinical settings.

中文翻译：

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使用运动捕捉的高密度脑电图电极位置的算法定位。

背景

脑电图 (EEG) 的准确源定位需要电极与大脑解剖学的共同配准，这一过程取决于 3D 头皮位置的精确测量。用于此类测量的手写笔数字化仪和基于相机的扫描仪要求对象保持静止，因此不适合幼儿或有运动障碍的人。

新方法

运动捕捉可准确测量一帧中的电极位置，但标记放置会显着增加设置时间，尤其是在高密度 EEG 中。我们开发了一种名为 MoLo 的算法，并作为开源 MATLAB 工具箱实现，以使用样条插值从运动捕捉中测量的位置子集计算 3D 电极坐标。算法准确性在 5 个不同大小的头部模型中进行了评估。

结果

MoLo 插值将 64 通道 EEG 的设置时间缩短了大约 10 分钟。平均电极插值误差为 2.95 ± 1.3 mm（范围：0.38–7.98 mm）。与真实电极位置相比，插值的源定位误差在 75% 和 35% 的偶极子中分别低于 1 毫米和 0.1 毫米。

与现有方法的比较

MoLo 的定位精度可与临床研究中常见的手写笔数字化仪和相机扫描仪相媲美。MoLo 算法可以与运动捕捉以外的其他工具一起部署，例如手写笔，以从测量位置的子集中提取高密度 EEG 电极位置。该算法对于涉及幼儿和其他无法长时间保持静止的研究特别有用。

结论

MoLo 的电极位置和源定位误差与支持其在研究和临床环境中测量高密度脑电图电极位置的其他方式类似。