Chaos and Time-Series Analysis
Chaos and Time-Series Analysis
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Modified global k-means algorithm for minimum sum-of-squares clustering problems
Pattern Recognition
EEG-based estimation of mental fatigue: convergent evidence for a three-state model
FAC'07 Proceedings of the 3rd international conference on Foundations of augmented cognition
Fuzzy C-means based clustering for linearly and nonlinearly separable data
Pattern Recognition
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This study investigates the behavioral indices of attention. A simple repetitive attentive task that resulted in mental fatigue was used consecutively in four trials. In the first step, reaction time and error responses were recorded to evaluate differences among trials. During the task, subjects showed different responses to stimulations. In the second part, to recognize the strategies, multiple clustering methods such as k-means and fuzzy c-means were performed in which behavioral indices and nonlinear features were used. In the last section, mental behavior was identified as a result of the chaotic properties of variations in reaction time. Therefore, the Lyapunov exponent of reaction times was evaluated. Results revealed that behavioral indices could distinguish attention from the occurrence of mental fatigue in trials. In addition, the three strategies used by subjects during the test protocol were assessed. Finally, variation of indices extracted from nonlinear analysis, that is, decrease in degree of chaotic behavior determined the transition from attention to mental fatigue. © 2012 Wiley Periodicals, Inc. Complexity, 2012 © 2012 Wiley Periodicals, Inc.