Proceedings of the ... Southern Biomedical Engineering Conference. Southern Biomedical Engineering Conference最新文献

The design of brain machine interfaces (BMI) has been improving over the past decade. Such improvements have led to advanced capability in terms of restoring the functionality of a paralyzed/amputated limb and producing fine controlled movements of a robotic arm and hand. However, there is still more to be invested towards producing advanced BMI features such as producing appropriate forces when gripping and carrying an object using an artificial limb. This feature requires direct supervision and control from the brain to produce accurate results. Toward this goal, this work investigates the processing of neural signals from four brain regions in a nonhuman primate to predict maximum grip force. The signals received from each of the primary motor (M1) cortex, primary somatosensory (S1) cortex, dorsal premotor (PmD) cortex, and ventral premotor (PmV) cortex are used to build regression models to predict the applied maximum grip force. Comparisons of model prediction results are presented. The relative prediction accuracy from all brain regions would assist in further investigation to build robust approaches for controlling the force values. The brain regions and their interactions could eventually be summed in a weighted manner to complete the targeted approach.

This paper presents the design, fabrication, and testing of a wireless heart rate (HR) monitoring device based on photoplethysmography (PPG) and smart devices. PPG sensors use infrared (IR) light to obtain vital information to assess cardiac health and other physiologic conditions. The PPG data that are transferred to a computer undergo further processing to derive the Heart Rate Variability (HRV) signal, which is analyzed to generate quantitative markers of the Autonomic Nervous System (ANS). The HRV signal has numerous monitoring and diagnostic applications. To this end, wireless connectivity plays an important role in such biomedical instruments. The photoplethysmograph consists of an optical sensor to detect the changes in the light intensity reflected from the illuminated tissue, a signal conditioning unit to prepare the reflected light for further signal conditioning through amplification and filtering, a low-power microcontroller to control and digitize the analog PPG signal, and a Bluetooth module to transmit the digital data to a Bluetooth-based smart device such as a tablet. An Android app is then used to enable the smart device to acquire and digitally display the received analog PPG signal in real-time on the smart device. This article is concluded with the prototyping of the wireless PPG followed by the verification procedures of the PPG and HRV signals acquired in a laboratory environment.

Increasingly accurate control of prosthetic limbs has been made possible by a series of advancements in brain machine interface (BMI) control theory. One promising control technique for future BMI applications is reinforcement learning (RL). RL based BMIs require a reinforcing signal to inform the controller whether or not a given movement was intended by the user. This signal has been shown to exist in cortical structures simultaneously used for BMI control. This work evaluates the ability of several common classifiers to detect impending reward delivery within primary somatosensory (S1) cortex during a grip force match to sample task performed by a nonhuman primate. The accuracy of these classifiers was further evaluated over a range of conditions to identify parameters that provide maximum classification accuracy. S1 cortex was found to provide highly accurate classification of the reinforcement signal across many classifiers and a wide variety of data input parameters. The classification accuracy in S1 cortex between rewarding and non-rewarding trials was apparent when the animal was expecting an impending delivery or an impending withholding of reward following trial completion. The high accuracy of classification in S1 cortex can be used to adapt an RL based BMI towards a user's intent. Real-time implementation of these classifiers in an RL based BMI could be used to adapt control of a prosthesis dynamically to match the intent of its user.

We introduce a rigorous mathematical theory for the analysis of local histograms, and consider the appropriateness of their use in the automated classification of textures commonly encountered in images of H&E stained tissues. We first discuss some of the many image features that pathologists indicate they use when classifying tissues, focusing on simple, locally-defined features that essentially involve pixel counting: the number of cells in a region of given size, the size of the nuclei within these cells, and the distribution of color within both. We then introduce a probabilistic, occlusion-based model for textures that exhibit these features, in particular demonstrating how certain tissue-similar textures can be built up from simpler ones. After considering the basic notions and properties of local histogram transforms, we then formally demonstrate that such transforms are natural tools for analyzing the textures produced by our model. In particular, we discuss how local histogram transforms can be used to produce numerical features that, when fed into mainstream classification schemes, mimic the baser aspects of a pathologist's thought process.