Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

Validation of cost-efficient EEG experimental setup for neural tracking in an auditory attention task


Baek,  Seung-Cheol
Research Group Neurocognition of Music and Language, Max Planck Institute for Empirical Aesthetics, Max Planck Society;
Center for Intelligent & Interactive Robotics, Artificial Intelligence and Robot Institute, Korea Institute of Science and Technology;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

(Publisher version), 3MB

Supplementary Material (public)
There is no public supplementary material available

Ha, J., Baek, S.-C., Lim, Y., & Chung, J. H. (2023). Validation of cost-efficient EEG experimental setup for neural tracking in an auditory attention task. Scientific Reports, 13: 22682. doi:10.1038/s41598-023-49990-6.

Cite as: https://hdl.handle.net/21.11116/0000-000E-3F27-1
When individuals listen to speech, their neural activity phase-locks to the slow temporal rhythm, which is commonly referred to as “neural tracking”. The neural tracking mechanism allows for the detection of an attended sound source in a multi-talker situation by decoding neural signals obtained by electroencephalography (EEG), known as auditory attention decoding (AAD). Neural tracking with AAD can be utilized as an objective measurement tool for diverse clinical contexts, and it has potential to be applied to neuro-steered hearing devices. To effectively utilize this technology, it is essential to enhance the accessibility of EEG experimental setup and analysis. The aim of the study was to develop a cost-efficient neural tracking system and validate the feasibility of neural tracking measurement by conducting an AAD task using an offline and real-time decoder model outside the soundproof environment. We devised a neural tracking system capable of conducting AAD experiments using an OpenBCI and Arduino board. Nine participants were recruited to assess the performance of the AAD using the developed system, which involved presenting competing speech signals in an experiment setting without soundproofing. As a result, the offline decoder model demonstrated an average performance of 90%, and real-time decoder model exhibited a performance of 78%. The present study demonstrates the feasibility of implementing neural tracking and AAD using cost-effective devices in a practical environment.