Towards fNIRS Hyperfeedback: A Feasibility Study on Real-Time Interbrain Synchrony
Kostorz, K.; Nguyen, T.; Pan, Y.; Melinscak, F.; Steyrl, D.; Hu, Y.; Sorger, B.; Hoehl, S.; Scharnowski, F.
Show abstract
Social interaction is of fundamental importance to humans. Prior research has highlighted the link between interbrain synchrony and positive outcomes in human social interaction. Neurofeedback is an established method to train ones brain activity and might offer a possibility to increase interbrain synchrony. Consequently, it would be advantageous to determine the feasibility of creating a neurofeedback system for enhancing interbrain synchrony to benefit human interaction. In this study, we investigated whether the most widely employed metric for interbrain synchrony, namely wavelet transform coherence, can be assessed accurately in near real-time using functional near-infrared spectroscopy (fNIRS), which is recognized for its mobility and ecological suitability for interactive research. To this end, we have undertaken a comprehensive approach encompassing simulations and a re-evaluation of two human-interaction datasets. Our findings indicate the potential for a stable near real-time measurement of wavelet transform coherence for integration durations of about one minute. This would align well with the methodology of an intermittent neurofeedback procedure. Our investigation lays the technical foundation for an fNIRS-based system to measure interbrain synchrony in near real-time. This advancement is crucial for the future development of a neurofeedback training system tailored to enhance interbrain synchrony to potentially benefit human interaction.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The impact of 1/f activity and baseline correction on the results and interpretation of time-frequency analyses of EEG/MEG data: A cautionary tale 96%
- Power spectrum slope confounds estimation of instantaneous oscillatory frequency 95%
- Predicting behavior through dynamic modes in resting-state fMRI data 95%
Similar papers in this journal
- Decoding continuous variables from event-related potential (ERP) data with linear support vector regression (SVR) using the Decision Decoding Toolbox (DDTBOX) 96%
- Demonstrating the need for long inter-stimulus intervals when studying the post-movement beta rebound following a simple button press 95%
- A sparse EEG-informed fMRI model for hybrid EEG-fMRI neurofeedback prediction 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.