Mirzaei Sepideh, Hosseini-Nejad Hossein, Sodagar Amir M
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:894-897. doi: 10.1109/EMBC44109.2020.9176515.
In this paper, a method for the detection and subsequently extraction of neural spikes in an intra-cortically recorded neural signal is proposed. This method distinguishes spikes from the background noise based on the natural difference between their time-domain amplitude variation patterns. According to this difference, a spike mask is generated, which takes on large values over the course of spikes, and much smaller values for the background noise. The "high" part of this mask is designed to be wide enough to contain a complete spike. By multiplying the input neural signal with the spike mask, spikes are amplified with a large factor while the background noise is not. The result is a spike-augmented signal with significantly larger signal-to-noise ratio, on which spike detection is performed much more easily and accurately. According to this detection mechanism, spikes of the original neural signal are extracted.Clinical Relevance-This paper presents an automatic spike detection technique, dedicated to brain-implantable neural recording devices. Such devices are developed for clinical applications such as the treatment of epilepsy, neuro-prostheses, and brain-machine interfacing for therapeutic purposes.
本文提出了一种用于检测并随后提取皮层内记录的神经信号中神经尖峰的方法。该方法基于尖峰与背景噪声在时域幅度变化模式上的自然差异来区分尖峰。根据这种差异，生成一个尖峰掩码，该掩码在尖峰过程中取值较大，而对于背景噪声取值要小得多。此掩码的“高”部分设计得足够宽以包含完整的尖峰。通过将输入神经信号与尖峰掩码相乘，尖峰被大幅放大，而背景噪声则不会。结果是一个信噪比显著更高的尖峰增强信号，在此信号上进行尖峰检测会更加容易和准确。根据这种检测机制，提取出原始神经信号的尖峰。临床相关性——本文提出了一种专门用于脑植入式神经记录设备的自动尖峰检测技术。此类设备是为癫痫治疗、神经假体以及用于治疗目的的脑机接口等临床应用而开发的。
