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

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

JOURNAL=Frontiers in Human Neuroscience

VOLUME=15

YEAR=2021

URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2021.636191

DOI=10.3389/fnhum.2021.636191

ISSN=1662-5161

ABSTRACT=<p>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.</p>