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Learning and memory involve a complex cognitive process to acquire, retain, and retrieve information in the central nervous system. However, the brain mechanism still needs to be well understood. This study aimed to examine the dynamic auditory verbal learning model of the brain mechanism involved in cognitive learning using the scale-free approach by the fractal analysis of electroencephalogram (EEG) data. This illustrates how the complexity of information processing in the brain changes while auditory and verbal learning occurs. Therefore, a standard verbal-auditory cognitive assessment test was used to create a learning paradigm. Eighteen healthy male volunteers (19–23 years old) were recruited and their verbal memories were assessed using the Rey auditory verbal learning test. Fifteen unrelated words were sequentially presented to the subjects and they were asked to recall the presented words as many as possible. The experiment was repeated five times with no stop in between. EEG recording was performed before, during and after each stage. Subsequently, the Hurst exponents of EEG were calculated and their associations with the recalled words and the learning rate were estimated. The approximate entropy was intended to confirm the Hurst exponent variations of signals. The statistical analysis of the data showed that the increase in the number of the recalled words was positively correlated with an increase in the Hurst exponents of EEG signals (more significant at the temporal channels) and a decrease in the approximate entropy of EEG signals during the learning of trials. These results denoted a reduced complexity pattern in EEG signals while rehearsing auditory and verbal memories. [ABSTRACT FROM AUTHOR]

Detecting chaos in heavy-noise environments is an important issue in many fields of science and engineering. In this paper, first, a new criterion is proposed to recognize chaos from noise based on the distribution of energy. Then, a new method based on stationary wavelet transform (SWT) is presented for chaos detection that is recommended for data that contain more than 60% noise. This method is dependent on the distribution of signal's energy in different frequency bands based on SWT for chaos detection which is robust to noisy environments. In this method, the effect of white noise and colored noise on the chaotic system is considered. As a case study, the proposed method is applied to detect chaos in two different oscillators based on memristor and memcapacitor. The simulation results are used to display the main points of the paper. [ABSTRACT FROM AUTHOR]