Department of Electronic & Electrical Engineering, University of Bath, Bath BA2 7AY, UK.
Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK.
J Neurosci Methods. 2021 Jan 1;347:108967. doi: 10.1016/j.jneumeth.2020.108967. Epub 2020 Oct 7.
Recording from the peripheral nervous system is key in the development of implantable neural interfaces. Despite a long history of using implantable electrodes for neuro-stimulation, it is difficult to make recordings from the nerves as signal amplitudes are often too small to be detected. Methods exist that are suitable for recording evoked potentials, but these require artificial stimulation of the nerve and thus have limited use in implanted neural interfaces.
In order to address these issues new methods are developed to analyse spontaneously occurring action potentials by extending an approach called velocity selective recording, which uses longitudinally spaced electrodes to record action potentials as they propagate. The new methods using image processing techniques to automatically identify and classify action potentials without any prior knowledge of their morphology.
Simulations are developed to test the methods, and a detailed experimental validation is performed using in-vivo recordings from the L5 dorsal rootlet of rat. Results show that this new approach can discriminate action potentials from both simulated and real recordings and the experimental validation demonstrates an ability to detect dermal stimulation by changes in the firing patterns of different axons.
This framework, unlike existing methods, is intrinsically suitable for recordings of spontaneous neural activity. Further it improves upon both the computational complexity and the overall performance of existing methods.
It is possible to perform on-line discrimination and identification of action potentials without any prior knowledge of their morphology using new image processing inspired methods.
记录外周神经系统是开发植入式神经接口的关键。尽管长期以来一直使用植入式电极进行神经刺激，但由于信号幅度通常太小而难以检测，因此很难从神经中进行记录。存在适用于记录诱发电位的方法，但这些方法需要对神经进行人工刺激，因此在植入式神经接口中使用受限。
为了解决这些问题，开发了新的方法来通过扩展一种称为速度选择记录的方法来分析自发发生的动作电位，该方法使用纵向间隔的电极来记录动作电位传播时的信号。新方法使用图像处理技术来自动识别和分类动作电位，而无需事先了解其形态。
开发了模拟来测试这些方法，并使用来自大鼠 L5 背根束的体内记录进行了详细的实验验证。结果表明，这种新方法可以区分模拟和真实记录中的动作电位，并且实验验证表明能够通过不同轴突的发射模式的变化来检测皮肤刺激。
与现有方法不同，该框架本质上适合于自发神经活动的记录。此外，它还提高了现有方法的计算复杂性和整体性能。
使用新的图像处理启发方法，可以在不了解其形态的情况下进行在线动作电位的判别和识别。
