Nonlinear dynamics and chaos theory are being widely used nowadays in neuroscience to characterize complex systems within which the change of the output isn't proportional to the change of the input. Nonlinear systems compared to linear systems, often appear chaotic, unpredictable, or counterintuitive, and yet their behaviour isn't random. The importance of the time series analysis, which exhibits a typical complex dynamics, within the area of nonlinear analysis can't be undermined. Hidden important dynamical properties of the physiological phenomenon can be detected by many features of these approaches. Nonlinear dynamics and chaos theory are being employed in neurophysiology with the aim to elucidate the complex brain activity from electroencephalographic (EEG) signals. The brain is a chaotic dynamical system and further, their generated EEG signals are generally chaotic in another sense, because, with respect to time, the amplitude changes continuously. A reliable and non-invasive measurement of memory load which will be made continuously while performing a cognitive task would be very helpful for assessing cognitive function, crucial for the prevention of decision-making errors, and also the development of adaptive user interfaces. Such a measurement could help to keep up the efficiency and productivity in task completion, work performance, and to avoid cognitive overload, especially in critical/high mental load workplaces like traffic control, military operations, and rescue commands. We have measured the linear and nonlinear dynamics of the EEG signals in subjects undergoing mental arithmetic task and measured the cognitive load on the brain continuously. We have also differentiated the subjects who can perform a mental task good and bad and developed a system using support vector machine to differentiate rest and task states.

中文翻译：

---

使用通过机器学习对脑电信号进行线性和非线性分析而提取的生物标志物来区分认知表现

如今，非线性动力学和混沌理论在神经科学中得到了广泛应用，以表征复杂系统，其中输出的变化与输入的变化不成比例。与线性系统相比，非线性系统通常看起来很混乱，不可预测或违反直觉，但是它们的行为并不是随机的。在非线性分析领域中，表现出典型复杂动力学特征的时间序列分析的重要性不可忽视。这些方法的许多特征可以检测到生理现象的隐藏的重要动力学特性。非线性动力学和混沌理论正在神经生理学中被采用，目的是从脑电图（EEG）信号阐明复杂的大脑活动。大脑是一个混沌的动力系统，而且，它们产生的EEG信号通常在另一种意义上是混沌的，因为相对于时间，振幅是连续变化的。在执行认知任务时将连续进行的可靠且无创的内存负载测量，对于评估认知功能，对防止决策错误至关重要以及开发自适应用户界面非常有帮助。这样的测量可以帮助保持任务完成，工作绩效的效率和生产率，并避免认知超负荷，尤其是在交通控制，军事行动和营救指挥等关键/高度精神负担的工作场所中。我们已经测量了正在执行心理算术任务的受试者的EEG信号的线性和非线性动力学，并连续测量了大脑的认知负荷。