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

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  • 1School of Mechanical Engineering, Pusan National University, Busan, South Korea.

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Summary

Researchers decoded hemodynamic responses to various sounds using functional near-infrared spectroscopy (fNIRS). Machine learning classified sound categories from brain activity, showing potential for subject-specific analysis without feature selection.

Keywords:
auditory cortexdecodingdeep learningfunctional near-infrared spectroscopy (fNIRS)long short-term memories (LSTMs)

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Understanding brain's auditory processing is crucial.
  • Hemodynamic responses (HRs) reflect neural activity.
  • Functional near-infrared spectroscopy (fNIRS) measures these responses non-invasively.

Purpose of the Study:

  • To decode hemodynamic responses evoked by diverse sound categories.
  • To investigate the potential of machine learning in classifying auditory stimuli based on fNIRS data.
  • To explore subject-wise classification without prior feature selection.

Main Methods:

  • Utilized functional near-infrared spectroscopy (fNIRS) to measure oxy-hemoglobin (HbO) concentration changes in the auditory cortex.
  • Presented 18 healthy subjects with six distinct sound categories (English, non-English, annoying, nature, music, gunshot) in 10-s blocks.
  • Employed Long Short-Term Memory (LSTM) networks for classification of the hemodynamic data.

Main Results:

  • Achieved a classification accuracy of 20.38 ± 4.63% for distinguishing between six sound categories.
  • LSTM network performance slightly exceeded chance levels.
  • Demonstrated successful subject-wise data classification, notably without the need for feature selection.

Conclusions:

  • It is possible to decode hemodynamic responses to different sound categories using fNIRS.
  • LSTM networks show potential for classifying auditory stimuli from brain activity.
  • Subject-specific classification without feature selection is feasible, paving the way for personalized auditory brain-computer interfaces.