A method for detection and classification of events in neural activity
A method for detection and classification of events in neural activity
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DOI：
10.1109/tbme.2006.877802
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发表时间：
2006-08-01
影响因子：
4.6
通讯作者：
Mitra, Partha P.
中科院分区：
文献类型：
--
作者：
Bokil, Hemant S.;Pesaran, Bijan;Mitra, Partha P.
我们提出了一种真实的时间预测的标点事件的神经活动，基于信号的时间-频谱，适用于连续的过程，如局部场电位（LFPs）以及尖峰列车。我们测试它的LFP和尖峰活动的记录获得以前从外侧顶内区（LIP）的猕猴执行记忆扫视任务。与早期的工作相比，在已知开始时间的试验进行分类，我们的方法直接从数据中检测和分类试验。它提供了一种手段，定量比较和对比的内容UP信号和尖峰列车：我们发现，检测器的性能的基础上LFP匹配的性能的基础上尖峰率。该方法应找到应用在基于LFP信号的神经假体的发展。我们的方法使用了一个新的特征向量，我们称之为二维倒谱。
We present a method for the real time prediction of punctuate events in neural activity, based on the time-frequency spectrum of the signal, applicable both,to continuous processes like local field potentials (LFPs) as well as to spike trains. We test it on recordings of LFP and spiking activity acquired previously from the lateral intraparietal area (LIP) of macaque monkeys performing a memory-saccade task. In contrast to earlier work, where trials with known start times were classified, our method detects and classifies trials directly from the data. It provides a means to quantitatively compare and contrast the content of UP signals and spike trains: we find that the detector performance based on the LFP matches the performance based on spike rates. The method should find application in the development of neural prosthetics based on the LFP signal. Our approach uses a new feature vector, which we call the 2d cepstrum.