Cross-Frequency Coupling Between Brain and Body Biosignals: A Systemic Physiology Augmented Functional Near-Infrared Spectroscopy Hyperscanning Study.

BACKGROUND: Understanding the brain and body processes during interaction or cooperation between two or more subjects is an important topic in current neuroscientific research. In a previous study, we introduced a novel approach that enables investigation of the coupling of biosignals (brain and systemic physiology, SP) from two subjects: systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS) hyperscanning. AIM: The aim was to extend our signal analysis approach by the cross-frequency time-dependent wavelet transform coherence (WTC) of the fNIRS and SP biosignals to gain new insights into the nature and cause of functional hyperconnectivity. SUBJECTS AND METHODS: 24 pairs of adults took part in a closed-eye versus prolonged eye-contact task of 10 min each. Brain and body activity was measured continuously by SPA-fNIRS hyperscanning. We calculated the time-dependent WTC of the biosignals for four different frequency bands: very low-frequency band (VLF, 0.002-0.08 Hz), low-frequency band 1 (LF1, 0.015-0.15 Hz), low-frequency band 2 (LF2, 0.08-0.15 Hz) and heart rate band (HR, 1-2 Hz). We then performed the cross-frequency correlated-coherence coupling analysis. RESULTS: A stronger cross-frequency coupling during the eye-contact condition (between 99 pairs of biosignals) was found than during the eye-closed condition (between 50 pairs of biosignals). Prolonged eye contact led to entrainment of the brain and body between different frequency bands and two subjects. The strongest hyperconnectivity was between the LF1-VLF frequency band. DISCUSSION AND CONCLUSION: With this exploratory study, we reveal further benefits of the SPA-fNIRS approach for future hyperscanning studies.

Machine Learning Distinguishes Familiar from Unfamiliar Pairs of Subjects Performing an Eye Contact Task: A Systemic Physiology Augmented Functional Near-Infrared Spectroscopy Hyperscanning Study.

BACKGROUND: Eye contact is an important aspect of human communication and social interactions. Changes in brain and systemic physiological activity associated with interactions between humans can be measured with systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS) hyperscanning, enabling inter-brain and inter-body synchronisation to be determined. In a previous study, we found that pairs of subjects that are socially connected show higher brain and body synchrony. AIM: To enable a deeper understanding, our aim was to build and automatically detect the best set of features to distinguish between two different groups (familiar and unfamiliar pairs). MATERIAL AND METHODS: We defined several features based on the Spearman correlation and wavelet transform coherence (WTC) of biosignals measured on 23 pairs of subjects (13 familiar and 10 unfamiliar pairs) during eye contact for 10 min. Additional custom features that identify the maximum brain-to-body coupling instants between pairs were generated. RESULTS: After testing on combinations of different feature extraction methods, four subsets of features with the strongest discrimination power were taken into account to train a decision tree (DT) machine learning (ML) algorithm. We have obtained 95.65% classification accuracy using a leave-one-out cross-validation. The coupling features which represent the two maximum mean values resulting from the sum of 7 time-dependent WTC signals (oxyhaemoglobin concentration of the right prefrontal region, total haemoglobin concentration of the left and right prefrontal region, heart rate, electrodermal activity on the left and right wrist, and skin temperature on the right wrist) played an essential role in the classification accuracy. CONCLUSION: Training the DT-ML algorithm with combined brain and systemic physiology data provided higher accuracy than training it only with brain or systemic data alone. The results demonstrate the power of the SPA-fNIRS hyperscanning approach and the potential in applying ML to investigate the strength of social bonds in a wide range of social interaction contexts.

Reducing False Alarm Rates in Neonatal Intensive Care: A New Machine Learning Approach.

In neonatal intensive care units (NICUs), 87.5% of alarms by the monitoring system are false alarms, often caused by the movements of the neonates. Such false alarms are not only stressful for the neonates as well as for their parents and caregivers, but may also lead to longer response times in real critical situations. The aim of this project was to reduce the rates of false alarms by employing machine learning algorithms (MLA), which intelligently analyze data stemming from standard physiological monitoring in combination with cerebral oximetry data (in-house built, OxyPrem). MATERIALS & METHODS: Four popular MLAs were selected to categorize the alarms as false or real: (i) decision tree (DT), (ii) 5-nearest neighbors (5-NN), (iii) naïve Bayes (NB) and (iv) support vector machine (SVM). We acquired and processed monitoring data (median duration (SD): 54.6 (± 6.9) min) of 14 preterm infants (gestational age: 26 6/7 (± 2 5/7) weeks). A hybrid method of filter and wrapper feature selection generated the candidate subset for training these four MLAs. RESULTS: A high specificity of >99% was achieved by all four approaches. DT showed the highest sensitivity (87%). The cerebral oximetry data improved the classification accuracy. DISCUSSION & CONCLUSION: Despite a (as yet) low amount of data for training, the four MLAs achieved an excellent specificity and a promising sensitivity. Presently, the current sensitivity is insufficient since, in the NICU, it is crucial that no real alarms are missed. This will most likely be improved by including more subjects and data in the training of the MLAs, which makes pursuing this approach worthwhile.