Hong Keum-Shik, Santosa Hendrik
Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea; School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea.
Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Republic of Korea.
Hear Res. 2016 Mar;333:157-166. doi: 10.1016/j.heares.2016.01.009. Epub 2016 Jan 29.
The ability of the auditory cortex in the brain to distinguish different sounds is important in daily life. This study investigated whether activations in the auditory cortex caused by different sounds can be distinguished using functional near-infrared spectroscopy (fNIRS). The hemodynamic responses (HRs) in both hemispheres using fNIRS were measured in 18 subjects while exposing them to four sound categories (English-speech, non-English-speech, annoying sounds, and nature sounds). As features for classifying the different signals, the mean, slope, and skewness of the oxy-hemoglobin (HbO) signal were used. With regard to the language-related stimuli, the HRs evoked by understandable speech (English) were observed in a broader brain region than were those evoked by non-English speech. Also, the magnitudes of the HbO signals evoked by English-speech were higher than those of non-English speech. The ratio of the peak values of non-English and English speech was 72.5%. Also, the brain region evoked by annoying sounds was wider than that by nature sounds. However, the signal strength for nature sounds was stronger than that for annoying sounds. Finally, for brain-computer interface (BCI) purposes, the linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were applied to the four sound categories. The overall classification performance for the left hemisphere was higher than that for the right hemisphere. Therefore, for decoding of auditory commands, the left hemisphere is recommended. Also, in two-class classification, the annoying vs. nature sounds comparison provides a higher classification accuracy than the English vs. non-English speech comparison. Finally, LDA performs better than SVM.
大脑听觉皮层区分不同声音的能力在日常生活中很重要。本研究调查了是否可以使用功能近红外光谱技术（fNIRS）来区分由不同声音引起的听觉皮层激活。在18名受试者暴露于四类声音（英语语音、非英语语音、恼人声音和自然声音）的同时，使用fNIRS测量了两个半球的血液动力学反应（HRs）。作为对不同信号进行分类的特征，使用了氧合血红蛋白（HbO）信号的平均值、斜率和偏度。关于与语言相关的刺激，在比非英语语音引起的大脑区域更广泛的区域观察到了可理解语音（英语）引起的HRs。此外，英语语音引起的HbO信号强度高于非英语语音。非英语和英语语音峰值的比率为72.5%。同样，恼人声音引起的大脑区域比自然声音引起的更广泛。然而，自然声音的信号强度比恼人声音更强。最后，出于脑机接口（BCI）的目的，将线性判别分析（LDA）和支持向量机（SVM）分类器应用于这四类声音。左半球的总体分类性能高于右半球。因此，对于听觉命令的解码，建议使用左半球。此外，在二类分类中，恼人声音与自然声音的比较比英语与非英语语音的比较提供了更高的分类准确率。最后，LDA的表现优于SVM。
