Lateralization of music processing with noises in the auditory cortex: an fNIRS study

Abstract

The present study is to determine the effects of background noise on the hemispheric lateralization in music processing by exposing 14 subjects to four different auditory environments: music segments only, noise segments only, music + noise segments, and the entire music interfered by noise segments. The hemodynamic responses in both hemispheres caused by the perception of music in 10 different conditions were measured using functional near-infrared spectroscopy. As a feature to distinguish stimulus-evoked hemodynamics, the difference between the mean and the minimum value of the hemodynamic response for a given stimulus was used. The right-hemispheric lateralization in music processing was about 75% (instead of continuous music, only music segments were heard). If the stimuli were only noises, the lateralization was about 65%. But, if the music was mixed with noises, the right-hemispheric lateralization has increased. Particularly, if the noise was a little bit lower than the music (i.e., music level 10~15%, noise level 10%), the entire subjects showed the right-hemispheric lateralization: This is due to the subjects' effort to hear the music in the presence of noises. However, too much noise has reduced the subjects' discerning efforts.

Impact of Noise Reduction Algorithm in Cochlear Implant Processing on Music Enjoyment

“…The music was presented without the addition of background noise. The noise reduction algorithm could enhance music enjoyment in background noise as the presence of the latter has cortical consequences in processing music; a functional near-infrared spectroscopy study found that the presence of background noise altered auditory cortex processing of music (23). The subjects were tested immediately following the introduction of noise reduction algorithm.…”

Bundled-Optode Method in Functional Near-Infrared Spectroscopy

“…First, a baseline-correction method is utilized to eliminate the drifting phenomena in the acquired fNIRS data [5,49]. In our work, a fourth order polynomial has been fit to the recorded HbX data of each channel, and the fitted curve has been subtracted from the measured HbX signal [50].…”

Optimal hemodynamic response model for functional near-infrared spectroscopy

“…Previous studies showed that localization of the cortical activation could be statistically estimated by fitting the estimated HRF to a pre-defined HRF (Hu et al, 2010; Kamran and Hong, 2014; Santosa et al, 2014). Let the optimal brain activation model is $\begin{array}{c}left{y}_{HbO}^i(k)=a_0^{\ast }+a_1^{\ast }HR{F}^{\ast }(k)+a_c^{\ast }\sin (2\pi f_c^{\ast }k)+a_r^{\ast }\sin (2\pi f_r^{\ast }k)\\ +a_m^{\ast }\sin (2\pi f_m^{\ast }k)+{\epsilon }^i(k),\end{array}$…”