Temporal convolutional neural networks to generate a head-related impulse response from one direction to another

Virtual sound synthesis is a technology that allows users to perceive spatial sound through headphones or earphones. However, accurate virtual sound requires an individual head-related transfer function (HRTF), which can be difficult to measure due to the need for a specialized environment. In this study, we proposed a method to generate HRTFs from one direction to the other. To this end, we used temporal convolutional neural networks (TCNs) to generate head-related impulse responses (HRIRs). To train the TCNs, publicly available datasets in the horizontal plane were used. Using the trained networks, we successfully generated HRIRs for directions other than the front direction in the dataset. We found that the proposed method successfully generated HRIRs for publicly available datasets. To test the generalization of the method, we measured the HRIRs of a new dataset and tested whether the trained networks could be used for this new dataset. Although the similarity evaluated by spectral distortion was slightly degraded, behavioral experiments with human participants showed that the generated HRIRs were equivalent to the measured ones. These results suggest that the proposed TCNs can be used to generate personalized HRIRs from one direction to another, which could contribute to the personalization of virtual sound

Alpha Phase Synchronization of Parietal Areas Reflects Switch-Specific Activity During Mental Rotation: An EEG Study

Action selection is typically influenced by the history of previously selected actions (the immediate motor history), which is apparent when a selected action is switched from a previously selected one to a new one. This history dependency of the action selection is even observable during a mental hand rotation task. Thus, we hypothesized that the history-dependent interaction of actions might share the same neural mechanisms among different types of action switching tasks. An alternative hypothesis is that the history dependency of the mental hand rotation task might involve a distinctive neural mechanism from the general action selection tasks so that the reported observation with the mental hand rotation task in the previously published literature might lack generality. To refute this possibility, we compared neural activity during action switching in the mental hand rotation with the general action switching task which is triggered by a simple visual stimulus. In the experiment, to focus on temporal changes in whole brain oscillatory activity, we recorded electroencephalographic (EEG) signals while 25 healthy subjects performed the two tasks. For analysis, we examined functional connectivity reflected in EEG phase synchronization and analyzed temporal changes in brain activity when subjects switched from a previously selected action to a new action. Using a clustering-based method to identify functional connectivity reflected in time-varying phase synchronization, we identified alpha-power inter-parietal synchronization that appears only during switching of the selected action, regardless of the hand laterality in the presented image. Moreover, the current study revealed that for both tasks the extent of this alpha-power inter-parietal synchronization was altered by the history of the selected actions. These findings suggest that alpha-power inter-parietal synchronization is engaged as a form of switching-specific functional connectivity, and that switching-related activity is independent of the task paradigm

Alpha Phase Synchronization of Parietal Areas Reflects Switch-Specific Activity During Mental Rotation: An EEG Study

Action selection is typically influenced by the history of previously selected actions (the immediate motor history), which is apparent when a selected action is switched from a previously selected one to a new one. This history dependency of the action selection is even observable during a mental hand rotation task. Thus, we hypothesized that the history-dependent interaction of actions might share the same neural mechanisms among different types of action switching tasks. An alternative hypothesis is that the history dependency of the mental hand rotation task might involve a distinctive neural mechanism from the general action selection tasks so that the reported observation with the mental hand rotation task in the previously published literature might lack generality. To refute this possibility, we compared neural activity during action switching in the mental hand rotation with the general action switching task which is triggered by a simple visual stimulus. In the experiment, to focus on temporal changes in whole brain oscillatory activity, we recorded electroencephalographic (EEG) signals while 25 healthy subjects performed the two tasks. For analysis, we examined functional connectivity reflected in EEG phase synchronization and analyzed temporal changes in brain activity when subjects switched from a previously selected action to a new action. Using a clustering-based method to identify functional connectivity reflected in time-varying phase synchronization, we identified alpha-power inter-parietal synchronization that appears only during switching of the selected action, regardless of the hand laterality in the presented image. Moreover, the current study revealed that for both tasks the extent of this alpha-power inter-parietal synchronization was altered by the history of the selected actions. These findings suggest that alpha-power inter-parietal synchronization is engaged as a form of switching-specific functional connectivity, and that switching-related activity is independent of the task paradigm

Transient increase in systemic interferences in the superficial layer and its influence on event-related motor tasks: a functional near-infrared spectroscopy study

Abstract. Functional near-infrared spectroscopy (fNIRS) is a widely utilized neuroimaging tool in fundamental neuroscience research and clinical investigation. Previous research has revealed that task-evoked systemic artifacts mainly originating from the superficial-tissue may preclude the identification of cerebral activation during a given task. We examined the influence of such artifacts on event-related brain activity during a brisk squeezing movement. We estimated task-evoked superficial-tissue hemodynamics from short source–detector distance channels (15 mm) by applying principal component analysis. The estimated superficial-tissue hemodynamics exhibited temporal profiles similar to the canonical cerebral hemodynamic model. Importantly, this task-evoked profile was also observed in data from a block design motor experiment, suggesting a transient increase in superficial-tissue hemodynamics occurs following motor behavior, irrespective of task design. We also confirmed that estimation of event-related cerebral hemodynamics was improved by a simple superficial-tissue hemodynamic artifact removal process using 15-mm short distance channels, compared to the results when no artifact removal was applied. Thus, our results elucidate task design-independent characteristics of superficial-tissue hemodynamics and highlight the need for the application of superficial-tissue hemodynamic artifact removal methods when analyzing fNIRS data obtained during event-related motor tasks

Improving Human Plateaued Motor Skill with Somatic Stimulation

Procedural motor learning includes a period when no substantial gain in performance improvement is obtained even with repeated, daily practice. Prompted by the potential benefit of high-frequency transcutaneous electrical stimulation, we examined if the stimulation to the hand reduces redundant motor activity that likely exists in an acquired hand motor skill, so as to further upgrade stable motor performance. Healthy participants were trained until their motor performance of continuously rotating two balls in the palm of their right hand became stable. In the series of experiments, they repeated a trial performing this cyclic rotation as many times as possible in 15 s. In trials where we applied the stimulation to the relaxed thumb before they initiated the task, most reported that their movements became smoother and they could perform the movements at a higher cycle compared to the control trials. This was not possible when the dorsal side of the wrist was stimulated. The performance improvement was associated with reduction of amplitude of finger displacement, which was consistently observed irrespective of the task demands. Importantly, this kinematic change occurred without being noticed by the participants, and their intentional changes of motor strategies (reducing amplitude of finger displacement) never improved the performance. Moreover, the performance never spontaneously improved during one-week training without stimulation, whereas the improvement in association with stimulation was consistently observed across days during training on another week combined with the stimulation. The improved effect obtained in stimulation trials on one day partially carried over to the next day, thereby promoting daily improvement of plateaued performance, which could not be unlocked by the first-week intensive training. This study demonstrated the possibility of effectively improving a plateaued motor skill, and pre-movement somatic stimulation driving this behavioral change

Forward Inverse Relaxation Model Incorporating Movement Duration Optimization

A computational trajectory formation model based on the optimization principle, which introduces the forward inverse relaxation model (FIRM) as the hardware and algorithm, represents the features of human arm movements well. However, in this model, the movement duration was defined as a given value and not as a planned value. According to considerable empirical facts, movement duration changes depending on task factors, such as required accuracy and movement distance thus, it is considered that there are some criteria that optimize the cost function. Therefore, we propose a FIRM that incorporates a movement duration optimization module. The movement duration optimization module minimizes the weighted sum of the commanded torque change term as the trajectory cost, and the tolerance term as the cost of time. We conducted a behavioral experiment to examine how well the movement duration obtained by the model reproduces the true movement. The results suggested that the model movement duration was close to the true movement. In addition, the trajectory generated by inputting the obtained movement duration to the FIRM reproduced the features of the actual trajectory well. These findings verify the use of this computational model in measuring human arm movements

EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials

Brain-machine interfaces (BMI) rely on the accurate classification of event-related potentials (ERPs) and their performance greatly depends on the appropriate selection of classifier parameters and features from dense-array electroencephalography (EEG) signals. Moreover, in order to achieve a portable and more compact BMI for practical applications, it is also desirable to use a system capable of accurate classification using information from as few EEG channels as possible. In the present work, we propose a method for classifying P300 ERPs using a combination of Fisher Discriminant Analysis (FDA) and a multiobjective hybrid real-binary Particle Swarm Optimization (MHPSO) algorithm. Specifically, the algorithm searches for the set of EEG channels and classifier parameters that simultaneously maximize the classification accuracy and minimize the number of used channels. The performance of the method is assessed through offline analyses on datasets of auditory ERPs from sound discrimination experiments. The proposed method achieved a higher classification accuracy than that achieved by traditional methods while also using fewer channels. It was also found that the number of channels used for classification can be significantly reduced without greatly compromising the classification accuracy

Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography

Many researchers have used machine learning models to control artificial hands, walking aids, assistance suits, etc., using the biological signal of electromyography (EMG). The use of such devices requires high classification accuracy of machine learning models. One method for improving the classification performance of machine learning models is normalization, such as z-score. However, normalization is not used in most EMG-based motion prediction studies, because of the need for calibration and fluctuation of reference value for calibration (cannot re-use). Therefore, in this study, we proposed a normalization method that combines sliding-window analysis and z-score normalization, that can be implemented in real-time processing without need for calibration. The effectiveness of this normalization method was confirmed by conducting a single-joint movement experiment of the elbow and predicting its rest, flexion, and extension movements from the EMG signal. The proposed normalization method achieved a mean accuracy of 64.6%, an improvement of 15.0% compared to the non-normalization case (mean of 49.8%). Furthermore, to improve practical applications, recent research has focused on reducing the user data required for model learning and improving classification performance in models learned from other people's data. Therefore, we investigated the classification performance of the model learned from other's data. Results showed a mean accuracy of 56.5% when the proposed method was applied, an improvement of 11.1% compared to the non-normalization case (mean of 44.1%). These two results showed the effectiveness of the simple and easy-to-implement method, and that the classification performance of the machine learning model could be improved

Identifying Single Trial Event-Related Potentials in an Earphone-Based Auditory Brain-Computer Interface

As brain-computer interfaces (BCI) must provide reliable ways for end users to accomplish a specific task, methods to secure the best possible translation of the intention of the users are constantly being explored. In this paper, we propose and test a number of convolutional neural network (CNN) structures to identify and classify single-trial P300 in electroencephalogram (EEG) readings of an auditory BCI. The recorded data correspond to nine subjects in a series of experiment sessions in which auditory stimuli following the oddball paradigm were presented via earphones from six different virtual directions at time intervals of 200, 300, 400 and 500 ms. Using three different approaches for the pooling process, we report the average accuracy for 18 CNN structures. The results obtained for most of the CNN models show clear improvement over past studies in similar contexts, as well as over other commonly-used classifiers. We found that the models that consider data from the time and space domains and those that overlap in the pooling process usually offer better results regardless of the number of layers. Additionally, patterns of improvement with single-layered CNN models can be observed