Effect of Body Positions on EEG signals in Mal de Debarquement Syndrome

Multi-Dimensional and Objective Assessment of Motion Sickness Susceptibility Based on Machine Learning

Background

As human transportation, recreation, and production methods change, the impact of motion sickness (MS) on humans is becoming more prominent. The susceptibility of people to MS can be accurately assessed, which will allow ordinary people to choose comfortable transportation and entertainment and prevent people susceptible to MS from entering provocative environments. This is valuable for maintaining public health and the safety of tasks.

Objective

To develop an objective multi-dimensional MS susceptibility assessment model based on physiological indicators that objectively reflect the severity of MS and provide a reference for improving the existing MS susceptibility assessment methods.

Methods

MS was induced in 51 participants using the Coriolis acceleration stimulation. Some portable equipment were used to digitize the typical clinical manifestations of MS and explore the correlations between them and Graybiel's diagnostic criteria. Based on significant objective parameters and selected machine learning (ML) algorithms, several MS susceptibility assessment models were developed, and their performances were compared.

Results

Gastric electrical activity, facial skin color, skin temperature, and nystagmus are related to the severity of MS. Among the ML assessment models based on these variables, the support vector machine classifier had the best performance with an accuracy of 88.24%, sensitivity of 91.43%, and specificity of 81.25%.

Conclusion

The severity of symptoms and signs of MS can be objectively quantified using some indicators. Multi-dimensional and objective assessment models for MS susceptibility based on ML can be successfully established.

Classification of Body Position During Muslim Prayer Using the Convolutional Neural Network

Background: Muslim prayer (Namaz) is the most important obligatory religious duty in Islam that is regularly performed five times per day at specific prescribed times by Muslims. Due to the fact that change of body position affects brain activity, Namaz can be considered as a suitable model to assess the effect of quick changes of the body position on brain activity measured by electroencephalography (EEG). Methods: Forty Muslim participants performed a four-cycle Namaz while their brain activity was being recorded using a 14-channel EEG recorder. The brain connectivity (as defined by a mutual correlation between EEG channels in this study) in different frequency bands (delta, theta, alpha, beta, and gamma) was measured in various positions of Namaz including standing, bowing, prostration, and sitting. Results: The results indicated that the delta band demonstrates the most changes in cross-correlation between the recorded channels, and finally, the accuracy of 73.8% was obtained in the data classification.