Validation of Cost-efficient EEG Experimental Setup for Neural Tracking in an Auditory Attention Task

Overview

Journal Sci Rep

Specialty Science

Date 2023 Dec 19

PMID 38114579

Authors

Jiyeon Ha

Seung-Cheol Baek

Yoonseob Lim

Jae Ho Chung

Affiliations

Soon will be listed here.

Abstract

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.

References

OSullivan J, Power A, Mesgarani N, Rajaram S, Foxe J, Shinn-Cunningham B . Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG. Cereb Cortex. 2014; 25(7):1697-706. PMC: 4481604. DOI: 10.1093/cercor/bht355. View

Mundanad Narayanan A, Bertrand A . Analysis of Miniaturization Effects and Channel Selection Strategies for EEG Sensor Networks With Application to Auditory Attention Detection. IEEE Trans Biomed Eng. 2019; 67(1):234-244. DOI: 10.1109/TBME.2019.2911728. View

Wang D, Chen J . Supervised Speech Separation Based on Deep Learning: An Overview. IEEE/ACM Trans Audio Speech Lang Process. 2019; 26(10):1702-1726. PMC: 6586438. DOI: 10.1109/TASLP.2018.2842159. View

Straetmans L, Holtze B, Debener S, Jaeger M, Mirkovic B . Neural tracking to go: auditory attention decoding and saliency detection with mobile EEG. J Neural Eng. 2021; 18(6). DOI: 10.1088/1741-2552/ac42b5. View

Aiken S, Picton T . Human cortical responses to the speech envelope. Ear Hear. 2008; 29(2):139-57. DOI: 10.1097/aud.0b013e31816453dc. View

Ciccarelli G, Nolan M, Perricone J, Calamia P, Haro S, OSullivan J . Comparison of Two-Talker Attention Decoding from EEG with Nonlinear Neural Networks and Linear Methods. Sci Rep. 2019; 9(1):11538. PMC: 6687829. DOI: 10.1038/s41598-019-47795-0. View

Power A, Colling L, Mead N, Barnes L, Goswami U . Neural encoding of the speech envelope by children with developmental dyslexia. Brain Lang. 2016; 160:1-10. PMC: 5108463. DOI: 10.1016/j.bandl.2016.06.006. View

Ding N, Simon J . Emergence of neural encoding of auditory objects while listening to competing speakers. Proc Natl Acad Sci U S A. 2012; 109(29):11854-9. PMC: 3406818. DOI: 10.1073/pnas.1205381109. View

Woldorff M, Gallen C, Hampson S, Hillyard S, Pantev C, Sobel D . Modulation of early sensory processing in human auditory cortex during auditory selective attention. Proc Natl Acad Sci U S A. 1993; 90(18):8722-6. PMC: 47430. DOI: 10.1073/pnas.90.18.8722. View

10.

Holle D, Meekes J, Bleichner M . Mobile ear-EEG to study auditory attention in everyday life : Auditory attention in everyday life. Behav Res Methods. 2021; 53(5):2025-2036. PMC: 8516794. DOI: 10.3758/s13428-021-01538-0. View

11.

Miran S, Akram S, Sheikhattar A, Simon J, Zhang T, Babadi B . Real-Time Tracking of Selective Auditory Attention From M/EEG: A Bayesian Filtering Approach. Front Neurosci. 2018; 12:262. PMC: 5938416. DOI: 10.3389/fnins.2018.00262. View

12.

Bleichner M, Mirkovic B, Debener S . Identifying auditory attention with ear-EEG: cEEGrid versus high-density cap-EEG comparison. J Neural Eng. 2016; 13(6):066004. DOI: 10.1088/1741-2560/13/6/066004. View

13.

Haghighi M, Moghadamfalahi M, Akcakaya M, Erdogmus D . EEG-assisted Modulation of Sound Sources in the Auditory Scene. Biomed Signal Process Control. 2019; 39:263-270. PMC: 6527367. DOI: 10.1016/j.bspc.2017.08.008. View

14.

Kaongoen N, Choi J, Jo S . Speech-imagery-based brain-computer interface system using ear-EEG. J Neural Eng. 2021; 18(1):016023. DOI: 10.1088/1741-2552/abd10e. View

15.

Presacco A, Miran S, Babadi B, Simon J . Real-Time Tracking of Magnetoencephalographic Neuromarkers during a Dynamic Attention-Switching Task. Annu Int Conf IEEE Eng Med Biol Soc. 2020; 2019:4148-4151. PMC: 7067200. DOI: 10.1109/EMBC.2019.8857953. View

16.

Han C, OSullivan J, Luo Y, Herrero J, Mehta A, Mesgarani N . Speaker-independent auditory attention decoding without access to clean speech sources. Sci Adv. 2019; 5(5):eaav6134. PMC: 6520028. DOI: 10.1126/sciadv.aav6134. View

17.

Calderone D, Lakatos P, Butler P, Castellanos F . Entrainment of neural oscillations as a modifiable substrate of attention. Trends Cogn Sci. 2014; 18(6):300-9. PMC: 4037370. DOI: 10.1016/j.tics.2014.02.005. View

18.

Mirkovic B, Debener S, Jaeger M, De Vos M . Decoding the attended speech stream with multi-channel EEG: implications for online, daily-life applications. J Neural Eng. 2015; 12(4):046007. DOI: 10.1088/1741-2560/12/4/046007. View

19.

Vanthornhout J, Decruy L, Wouters J, Simon J, Francart T . Speech Intelligibility Predicted from Neural Entrainment of the Speech Envelope. J Assoc Res Otolaryngol. 2018; 19(2):181-191. PMC: 5878153. DOI: 10.1007/s10162-018-0654-z. View

20.

Schmitt R, Meyer M, Giroud N . Better speech-in-noise comprehension is associated with enhanced neural speech tracking in older adults with hearing impairment. Cortex. 2022; 151:133-146. DOI: 10.1016/j.cortex.2022.02.017. View