Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study - Yoo, So-Hyeon; Santosa, Hendrik; Kim, Chang-Seok; Hong, Keum-Shik (Frontiers in Human Neuroscience, 2021)
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-sec 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 classes classification. Though LSTM networks’ performance was a little higher than chance levels, it is noteworthy that we could classify the data subject-wisely without feature selections.