[Effects of sampling parameter variation on the complexity analysis of EEG].
The algorithmic complexity and the approximate entropy of EEG were calculated and analyzed with different data points, different sample frequencies and different sample time duration. The results showed that under fixed sample frequency, the longer the data was, the more stable the complexity values were. With fixed sample time duration or fixed data point, lower sample frequency would be better both for EEG distinguishing and for computing time saving.
Feng
Zhouyan
Z
College of Life Science, Zhejiang University, Hangzhou 310027.
Zheng
Xiaoxiang
X
D000465:Algorithms; D000818:Animals; D004569:Electroencephalography / Q000379:methods*; D019277:Entropy; D051381:Rats; D017207:Rats, Sprague-Dawley; D012815:Signal Processing, Computer-Assisted