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April 28, 2021Frontiers in Human NeuroscienceOpen Access

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

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Why the study?

This study aims to decode hemodynamic responses evoked by multiple sound-categories using functional near-infrared spectroscopy.

Can LSTM networks decode hemodynamic responses evoked by multiple sound categories using fNIRS in healthy subjects?

Population

18 healthy subjects

Comparison

10-s blocks of six sound-categories (English, non-English, annoying, nature, music, and gunshot)

Design

fNIRS decoding study using Long short-term memory networks

Authors

SYSo-Hyeon YooHSHendrik SantosaCKChang‐Seok Kim

Discussion

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Overview

fNIRS-LSTM decoding of sound-evoked HRs is feasible; leaves open validation for auditory neuroscience applications.

Structured PICO

Can LSTM networks decode hemodynamic responses evoked by multiple sound categories using fNIRS in healthy subjects?

P
Population
18 healthy subjects
I
Intervention
Listening to 10-s blocks of six sound-categories (English, non-English, annoying, nature, music, and gunshot) with hemodynamic responses decoded using Long short-term memory (LSTM) networks
O
Outcome
Classification accuracy of decoding hemodynamic responsessurrogate

LSTM networks can decode fNIRS data of multiple sound categories subject-wise without feature selection, achieving slightly above chance level accuracy.

Cite This Study

Yoo et al. (2021) studied this question.

synapsesocial.com/papers/6a6fffa3af0c21e93927e9f3https://doi.org/10.3389/fnhum.2021.636191
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