Nonlinear dynamics and chaos theory are being widely used nowadays in neuroscience to characterize complex systems within which the change of the output is not proportional to the change applied at the input. Such nonlinear systems compared to linear systems, often appear chaotic, unpredictable, or counterintuitive, however, yet their behaviour is not mapped out as random. Thus, hidden potential of the dynamical properties of the physiological phenomenon can be detected by these approaches especially to elucidate the complex human brain activity gathered from the electroencephalographic (EEG) signals. As it is known, brain is a chaotic dynamical system and its generated EEG signals are generally chaotic because, with respect to time, the amplitude changes continuously. A reliable and non-invasive measurement of memory load, to measure continuously while performing a cognitive task, is highly desirable to assess cognitive functions, crucial for prevention of decision-making errors. Such measurements help to keep up the efficiency and productivity in task completion, work performance, and to avoid cognitive overload, especially at high mental or physical workload places like traffic control, military operations, and rescue commands. In this work, we have measured the linear and nonlinear dynamics of the EEG signals in subjects undergoing mental arithmetic task. Further, we have also differentiated the subjects who can perform a mental task good or bad, and developed a hybrid machine learning model, the SMORASO-DT (SMOte + Random forest + lASso- Decision Tree), to differentiate good and bad performers during n-back task state with an accuracy rate of 78%.

中文翻译：

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SMORASO-DT：一种混合式机器学习分类模型，用于基于心算任务中的工作记忆负荷对个人进行分类

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，此类非线性系统通常看起来很混乱，不可预测或违反直觉，但是它们的行为并未映射为随机的。因此，可以通过这些方法来检测生理现象的动力学特性的隐藏潜力，尤其是阐明从脑电图（EEG）信号中收集到的复杂人脑活动。众所周知，大脑是一个混沌的动力学系统，其产生的EEG信号通常是混沌的，因为相对于时间，振幅会连续变化。可靠且无创的内存负载测量，在执行认知任务时进行连续测量，非常需要评估认知功能，这对于防止决策错误至关重要。这样的测量有助于保持任务完成，工作绩效的效率和生产率，并避免认知负担过重，尤其是在交通或交通管制，军事行动和救援命令等精神或身体负荷较高的地方。在这项工作中，我们测量了正在执行心理算术任务的受试者的脑电信号的线性和非线性动力学。此外，我们还区分了可以完成心理任务的对象好坏，并开发了混合机器学习模型SMORASO-DT（SMOte +随机森林+ lASso-决策树），以区分在n返回任务状态，准确率为78％。