Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study

So-Hyeon Yoo; Hendrik Santosa; Chang-Seok Kim; Keum-Shik Hong

doi:10.3389/fnhum.2021.636191

Frontiers in Human Neuroscience (Apr 2021)

Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study

So-Hyeon Yoo,
Hendrik Santosa,
Chang-Seok Kim,
Keum-Shik Hong

Affiliations

So-Hyeon Yoo: School of Mechanical Engineering, Pusan National University, Busan, South Korea
Hendrik Santosa: Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States
Chang-Seok Kim: Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea
Keum-Shik Hong: School of Mechanical Engineering, Pusan National University, Busan, South Korea

DOI: https://doi.org/10.3389/fnhum.2021.636191
Journal volume & issue: Vol. 15

Abstract

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This study aims to decode the hemodynamic responses (HRs) evoked by multiple sound-categories using functional near-infrared spectroscopy (fNIRS). The six different sounds were given as stimuli (English, non-English, annoying, nature, music, and gunshot). The oxy-hemoglobin (HbO) concentration changes are measured in both hemispheres of the auditory cortex while 18 healthy subjects listen to 10-s blocks of six sound-categories. Long short-term memory (LSTM) networks were used as a classifier. The classification accuracy was 20.38 ± 4.63% with six class classification. Though LSTM networks’ performance was a little higher than chance levels, it is noteworthy that we could classify the data subject-wise without feature selections.

Published in Frontiers in Human Neuroscience

ISSN: 1662-5161 (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
Website: http://www.frontiersin.org/human_neuroscience

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