Basic Scale Entropy Method Applied to Analyze Complexity of Different Brain Electrical Activities

In order to overcome the drawbacks of most chaos-based methods-used in time series analysis and especially used in analyzing brain electrical activity, we introduce a new complexity measuring method based on the basic scale entropy, which is simple but effective in anti-jamming and can analyze short-term time series, and exactly overcomes the main drawbacks of most chaos-based methods. And we use a group of data to test the efficiency of this method, which consists of five sets (denoted A-E), each containing 100 single-channel EEG signals of 23.6 s. Each signal has been selected after visual inspection for artifacts and has passed a weak stationarity criterion. Sets A and B have been taken from surface EEG recordings of five healthy volunteers with eyes open and closed, respectively. Signals in two sets have been measured in seizure-free intervals from five patients in the epileptogenic zone (D) and from the hippocampal formation of the opposite hemisphere of the brain (C). Set E contains seizure activity, selected from all recording sites exhibiting ictal activity. The results generally show that when the brain is acted consciously, the more complicated the brain acts, the larger the value of basic scale entropy is.