Case study of interrelation between brain-computer interface based multimodal metric and heart rate variability

Abstract

The Brain-Computer Interface (BCI) can be used for evaluation of the state of individuals during everyday routines. As shown in previous works, there is a relationship between the BCI multimodal metric with functional states of human. We have used power of Theta, Alpha, Beta low and Beta high electroencephalography rhythms and head motion data signals for multimodal metric. Heart Rate Variability (HRV) is common medical method for functional state assessment. In this paper the results of interrelation estimation between multimodal metric and HRV are shown. We used Pearson correlation coefficient (PCC) for estimates of interrelation between multimodal metric and HRV. It was found, the best results for estimates of parasympathetic part of the autonomic nervous system and suprasegmental regulation HRV have value of PCC more then critical value for Pearson correlation. © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.