Mccraybrowning3783

Since a vibrator needs to be pressed onto the osseous parts of the head, bone-conduction (BC) is often accompanied by pain and esthetic problems. In order to solve these problems, "distant presentation" has been proposed. In the distant presentation, vibrators are presented to the neck, upper limb or trunk. Our previous studies focused on the perception and propagation characteristics of distantly-presented BC sound in the ultrasonic range and an application to a novel audio-interface. On the other hand, a limited number of studies have been conducted on distantly-presented BC in the audible-frequency range. In this study, to examine the basic properties of the distantly-presented BC perception in the audible-frequency range, hearing thresholds, difference limens for frequency (DLFs) and temporal modulation transfer functions (TMTFs) were measured under the condition that AC sounds were insulated sufficiently. The results obtained indicated that BC sounds can be clearly perceived at distal parts of the body even in the audible-frequency range and no significant degradation of frequency and temporal information occurs in the propagation process in the body.Patients with Parkinson's disease (PD) can be divided into two subtypes based on clinical features, namely tremor-dominant (TD) and postural instability and gait difficulty (PIGD). Detection of PIGD symptoms is crucial for early diagnosis of PD and timely clinical intervention. However, patients at the early stage may not exhibit obvious motor dysfunctions during normal straight walking leading to difficulties in PD identification. Researchers have found that patients would show significant motor deteriorations in turning due to their cognition limitation. Therefore, turning detection is essential for quantitative motion analysis in the gait assessment of PD patients. In this study, we proposed a novel inertial-sensor-based algorithm for turning detection. Ten healthy young participants were enrolled in the experiment where they were required to walk along a 7-meter pathway with two 180 degree turns at their comfortable walking speed. Five inertial sensors were attached to the upper trunk, the shank and the foot of both legs. The algorithm performance was validated using an optical motion capture system for reference and two sensor combination options (upper trunk and shank sensors, upper trunk and foot sensors) were compared. The results showed that the proposed algorithm achieved accuracy over 98% for identifying the turning state of both legs. The integration of the upper trunk and foot sensors had no significant effect on the detection accuracy compared to that with the use of the upper trunk and shank sensors. Our algorithm has the potential to be implemented in the motion analysis model for complicated gait tasks, which has great potential in the early diagnosis of PIGD.The isometric contraction is the most investigated muscle contraction, however most tasks in daily life involve anisometric contractions. Most hand prostheses studies [1] use sEMG features to directly relate the exerted force as a means of intuitive control. It may thus be expected that similar sEMG-velocity relationships characterizing anisometric contractions may also contribute towards intuitive prosthetic hand control. While different contraction type relationships have been studied separately, in this work anisometric and isometric contraction experiments on the biceps brachii muscle were carried out using the same sEMG electrode system and the motor unit activity was then related to limb velocities and limb forces, to respectively characterize the isometric and anisometric contractions. Climbazole purchase This muscle was chosen as a simpler alternative to the synergistic hand muscles as an initial test of the general concept.Clinical Relevance- These contraction characterizations with sEMG may be used to afford prosthetic intuitive control and to assist in motor impairment diagnosis and rehabilitation.Hyperdimensional computing is a promising novel paradigm for low-power embedded machine learning. It has been applied on different biomedical applications, and particularly on epileptic seizure detection. Unfortunately, due to differences in data preparation, segmentation, encoding strategies, and performance metrics, results are hard to compare, which makes building upon that knowledge difficult. Thus, the main goal of this work is to perform a systematic assessment of the HD computing framework for the detection of epileptic seizures, comparing different feature approaches mapped to HD vectors. More precisely, we test two previously implemented features as well as several novel approaches with HD computing on epileptic seizure detection. We evaluate them in a comparable way, i.e., with the same preprocessing setup and with identical performance measures. We use two different datasets in order to assess the generalizability of our conclusions. The systematic assessment involved three primary aspects relevant for potential wearable implementations 1) detection performance, 2) memory requirements, and 3) computational complexity. Our analysis shows a significant difference in detection performance between approaches, but also that the ones with the highest performance might not be ideal for wearable applications due to their high memory or computational requirements. Furthermore, we evaluate a post-processing strategy to adjust the predictions to the dynamics of epileptic seizures, showing that performance is significantly improved in all the approaches and also that after post-processing, differences in performance are much smaller between approaches.Since neurons have temperature sensitive properties, gold nanorod (GNR)-mediated photothermal stimulation has been developed as a neuromodulation application. As an in vitro photothermal platform, GNR-layer was integrated with substrates to effectively apply heat stimulation to the cultured neurons. However, identifying optimal laser power for a targeted temperature on the substrate requires the consideration of thermal properties of the GNR-coated substrates. In this report, we suggest a simple numerical method to determine incident laser power on the substrates for a targeted temperature.Neural development of infants has drawn increasing research interests from the community. In this paper, we investigated the frequency band power of 112 infants who participated in an auditory oddball experiment, and the visual expectation (VE) score of 177 infants who went through a visual expectation paradigm test. Analysis found that the frequency band power decreases in the delta and theta bands, and increases in the alpha and beta bands when the infants grow up from 6 months old to 18 months old. We also proposed a sustainability index to measure the capability of a subject to maintain their band power in the auditory oddball experiment when infants grow up from 6 months old to 18 months old. Analysis shows that the sustainability index increased significantly in the alpha and beta band, decreased in the delta and theta bands. Correlation between the VE score and frequency band power was investigated on 47 infants who participated in both auditory oddball experiment and visual expectation paradigm test. Analysis shows that the reaction speed to stimulus have statistical a significant correlation with the changes of band power and sustainability index in posterior and temporal section, and in the higher frequency bands.Deep brain stimulation (DBS) therapy has been widely used in clinical practice for the treatment of neurological diseases and has achieved significant therapeutic effect. In this paper, aiming at the social problem of drug addiction, we design an electrical stimulation system which can be used in animal experiments, carry out the memory extinction experiment of addiction in rats, and explore the effective electrical stimulation parameters. The DBS system consists of a rechargeable battery and a PCB stimulation circuit composed of discrete devices. In animal experiments, the power consumption of the circuit is 0.36mW in the electrical stimulation stage. Theoretically, the circuit can work continuously for more than 100 days with a 3.7V 250mAh lithium battery. The stimulation circuit is highly programmable and the output stimulation current ranges from 100μA to 5000μA with a 20μA current resolution.We discuss the practical employment of a machine learning (ML) technique within AI for a social good application. We present an application for elderly adult dementia onset prognostication. First, the paper explains our encouraging preliminary study results of EEG responses analysis using a signal complexity measure of multiscale entropy (MSE) in reminiscent interior working memory evaluation tasks. Then, we compare shallow and deep learning machine learning models for a digital biomarker of dementia onset detection. The evaluated machine-learning models succeed in the most reliable median accuracies above 80% using random forest and fully connected neural network classifiers in automatic discrimination of normal cognition versus a mild cognitive impairment (MCI) task. The classifier input features consist of MSE patterns only derived from four dry EEG electrodes. Fifteen elderly subjects voluntarily participate in the reported study focusing on EEG-based objective dementia biomarker advancement. The results showcase the essential social advantages of artificial intelligence (AI) application for the dementia prognosis and advance ML for the subsequent use for simple objective EEG-based examination.Clinical relevance- This manuscript introduces an objective biomarker from EEG recorded by a wearable for a plausible replacement of a mild cognitive impairment (MCI) evaluation using usual biased paper and pencil examinations.Brain machine interface (BMI) can translate neural activity into digital commands to control prostheses. The decoder in BMI models the mechanism relating to neural activity and intents in brain. In our brain, single neuronal tuning property and neural connectivity contribute to encoding the intents together. These properties may change, a phenomenon which is named neural adaptation during using BMIs. Neural adaptation requires the decoder to consider the two factors at the same time and has the potential to follow their changes. However, in the previous work, the class of neural network and clustering decoder can consider the neural connectivity regardless of the single neuronal tuning property. On the other hand, point process methods can model the single neuronal tuning property but fail to address the neural connectivity. In this paper, we propose a new point process decoder with the information of neural connectivity named NCPP. We derive the neural connectivity component from the point process method by Bayes' rule and use a clustering decoder to represent the neural connectivity. This method can consider the neural connectivity and the single neuronal tuning property at the same time. We validate the method on simulation data where the point process method cannot achieve a good decoding performance and compare it with sequential Monte Carlo point process method (SMCPP). The results show our method outperforms the pure point process method which indicates our method can model the neural connectivity and single neuronal tuning property at the same time.Clinical Relevance-This paper proposes a decoder that can model the neural connectivity and the single neuronal tuning property at the same time, which is potential to explain the neural adaptation computationally.