Yoo So-Hyeon, Santosa Hendrik, Kim Chang-Seok, Hong Keum-Shik
School of Mechanical Engineering, Pusan National University, Busan, South Korea.
Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States.
Front Hum Neurosci. 2021 Apr 28;15:636191. doi: 10.3389/fnhum.2021.636191. eCollection 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-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.
本研究旨在使用功能近红外光谱技术（fNIRS）解码由多种声音类别诱发的血流动力学反应（HRs）。六种不同的声音作为刺激呈现（英语、非英语、烦人的声音、自然声音、音乐和枪声）。在18名健康受试者聆听六种声音类别各10秒的片段时，测量听觉皮层两个半球的氧合血红蛋白（HbO）浓度变化。长短期记忆（LSTM）网络被用作分类器。六分类的分类准确率为20.38±4.63%。虽然LSTM网络的表现略高于随机水平，但值得注意的是，我们可以在不进行特征选择的情况下按受试者对数据进行分类。
