Abstract
Biological systems, composed of various interrelated components, are nonlinear systems. Improved disease diagnosis and the application of efficient treatment and therapeutic aids are the direct outcomes of possessing a deep understanding of such systems. Therefore, by employing diverse biological system simulations and subsequently analyzing their responses and characteristics, we can diagnose diseases. In this particular study, a novel stimulation method was utilized for the first time, employing the Rossler equation, to record the electromyogram (EMG) signals of the biceps muscle in ten participants. The presented dataset enables the extraction of biological, computational, and chaotic features, which can be utilized for disease classification and diagnosis. Furthermore, this dataset can be employed for the training, validation, and testing of neural networks.