中川 誠司

Thanks to affordable 3D printers, creating complex designs like anatomically accurate dummy heads is now accessible. This study introduces dummy heads with 3D-printed skulls and silicone skins to explore crosstalk cancellation in bone conduction (BC). Crosstalk occurs when BC sounds from a transducer on one side of the head reach the cochlea on the opposite side. This can disrupt binaural cues essential for sound localization and speech understanding in noise for individuals using BC hearing devices. We provide a step-by-step guide to constructing the dummy head and demonstrate its application in canceling crosstalk. The 3D models used in this study are freely available for replication and further research. Several dummy heads were 3D-printed using ABS for the skull and silicone skins of varying hardness, with a 3-axis accelerometer at the cochlea location to simulate inner ear response. Since the cochlea is inaccessible in humans, we targeted crosstalk cancellation at the mastoid, assessing if this cancellation extended to the cochlea within the dummy heads. We compared these results with our previous experiments conducted on seven human subjects, who had their hearing thresholds measured with and without crosstalk cancellation, to evaluate if the dummy heads could reliably replicate human crosstalk cancellation effects.

The medial olivocochlear reflex (MOCR) is reported to be modulated by the predictability of an upcoming sound occurrence. Here the relationship between MOCR and internal confidence in temporal anticipation evaluated by reaction time (RT) was examined. The timing predictability of the MOCR elicitor was manipulated by adding jitters to preceding sounds. MOCR strength/RT unchanged in a small (10%) jitter condition, and decrease/increase significantly in the largest (40%) jitter condition compared to the without-jitter condition. The similarity indicates that the MOCR strength reflects confidence in anticipation, and that the predictive control of MOCR and response execution share a common neural mechanism.

Rhythms are the most natural cue for temporal anticipation because many sounds in our living environment have rhythmic structures. Humans have cortical mechanisms that can predict the arrival of the next sound based on rhythm and periodicity. Herein, we showed that temporal anticipation, based on the regularity of sound sequences, modulates peripheral auditory responses via efferent innervation. The medial olivocochlear reflex (MOCR), a sound-activated efferent feedback mechanism that controls outer hair cell motility, was inferred noninvasively by measuring the suppression of otoacoustic emissions (OAE). First, OAE suppression was compared between conditions in which sound sequences preceding the MOCR elicitor were presented at regular (predictable condition) or irregular (unpredictable condition) intervals. We found that OAE suppression in the predictable condition was stronger than that in the unpredictable condition. This implies that the MOCR is strengthened by the regularity of preceding sound sequences. In addition, to examine how many regularly presented preceding sounds are required to enhance the MOCR, we compared OAE suppression within stimulus sequences with 0-3 preceding tones. The OAE suppression was strengthened only when there were at least three regular preceding tones. This suggests that the MOCR was not automatically enhanced by a single stimulus presented immediately before the MOCR elicitor, but rather that it was enhanced by the regularity of the preceding sound sequences.

Fluid-filled fractures involving kinks and branches result in complex interactions between Krauklis waves-highly dispersive and attenuating pressure waves within the fracture-and the body waves in the surrounding medium. For studying these interactions, we introduce an efficient 2D time-harmonic elastodynamic boundary element method. Instead of modeling the domain within a fracture as a finite-thickness fluid layer, this method employs zero-thickness, poroelastic Linear-Slip Interfaces to model the low-frequency, local fluid-solid interaction. Using this method, the scattering of Krauklis waves by a single kink along a straight fracture and the radiation of body waves generated by Krauklis waves within complex fracture systems are examined.

Brain computer interfaces based on speech imagery have attracted attention in recent years as more flexible tools of machine control and communication. Classifiers of imagined speech are often trained for each individual due to individual differences in brain activity. However, the amount of brain activity data that can be measured from a single person is often limited, making it difficult to train a model with high classification accuracy. In this study, to improve the performance of the classifiers for each individual, we trained variational autoencoders (VAEs) using magnetoencephalographic (MEG) data from seven participants during speech imagery. The trained encoders of VAEs were transferred to EEGNet, which classified speech imagery MEG data from another participant. We also trained conditional VAEs to augment the training data for the classifiers. The results showed that the transfer learning improved the performance of the classifiers for some participants. Data augmentation also improved the performance of the classifiers for most participants. These results indicate that the use of VAE feature representations learned using MEG data from multiple individuals can improve the classification accuracy of imagined speech from a new individual even when a limited amount of MEG data is available from the new individual.