RAIDERS 2019 Abstracts

Full Papers
Paper Nr: 1

Validation of the fNIRS Pioneer™, a Portable, Durable, Rugged functional Near-Infrared Spectroscopy (fNIRS) Device


Bethany K. Bracken, Elena K. Festa, Hsin-Mei Sun, Calvin Leather and Gary Strangman

Abstract: Assessing cognitive workload using functional near-infrared spectroscopy (fNIRS) in labs is well established. However, fNIRS sensors useful during normal activities in real-world environments are only recently emerging. We validated a small, portable fNIRS sensor (the fNIRS Pioneer ™) against a larger sensor with coverage of a larger cortical area, the NINScan developed at Massachusetts General Hospital. We used a gold-standard working memory task (n-back; (Kirchner, 1958)) and a more complex multi-attribute task battery (MATB) (Santiago-Espada et al., 2011). Twenty healthy adult (21.5 ± 3.3 years; 9 males) students at Brown University completed all three experimental visits. Fitting with previous research, on the n-back task, we found a significant effect of difficulty level on blood oxygenation (HbO2) in dorsolateral prefrontal cortex (dlPFC) HbO2 (p<.01), but not medial PFC HbO2 with the fNIRS Pioneer. For the NINScan, we observed increases in HbO2 from 1- to 2- to 3-back in two channels corresponding to the border between ventrolateral PFC (vlPFC) and dlPFC in both hemispheres (p<.05). When we aggregated MATB data across subtasks, and after accounting for time-on-task, we found a significant (p<.01) effect on HbO2 for the Pioneer and the NINScan. In all cases, the significant HbO2 findings were negative relationships, indicating less brain activation with better performance. While prior literature of functional brain imaging with MATB is not available, this finding is at least broadly consistent with the role of lateral PFC’s role in working memory. This indicates that both the fNIRS Pioneer and the NINScan sensor, when combined with appropriate data analytic techniques were useful for detecting changes in HbO2 that correlate with cognitive workload and behaviour, and that the fNIRS Pioneer is able to assess cognitive workload similarly to more larger, more expensive, and more established devices.

Paper Nr: 2

Case Study of Interrelation between Brain-Computer Interface based Multimodal Metric and Heart Rate Variability


V. S. Vasilyev, V. I. Borisov, A. M. Syskov and V. S. Kublanov

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.

Paper Nr: 3

Towards Simplifying Assessment of Athletes Physical Fitness: Evaluation of the Total Physical Performance by Means of Machine Learning


Vladimir Kublanov, Anton Dolganov, Viktoriya Badtieva and David Akopyan

Abstract: The paper describes the methodology for the evaluation of the total physical performance of athletes on the basis of simultaneously recorded signals of stabilography and heart rate variability. An objective assessment of the level of physical performance was carried out using testing on the bicycle ergometer. The use of genetic programming and linear discriminant analysis allowed obtaining the set of diagnostically significant features. The set of diagnostically significant features is able to determine the level of physical fitness using only data from stabilographic studies and heart rate variability with an accuracy of at least 97%. Strength and weaknesses of the proposed approach are discussed.