Current Issue : January - March Volume : 2017 Issue Number : 1 Articles : 5 Articles
All neural information systems (NIS) rely on sensing neural activity to supply commands\nand control signals for computers, machines and a variety of prosthetic devices. Invasive systems\nachieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused\nby tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes\nprovides excellent detection of a broad range of frequency oscillatory activities through the placement\nof a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless\n32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates.\nThe system is designed to monitor brain sensorimotor rhythms and present current stimuli with a\nconfigurable duration, frequency and amplitude in real time to the brain based on the brain activity\nreport. The battery is charged via a novel ultrasonic wireless power delivery module developed\nfor efficient delivery of power into a deeply-implanted system. The system was successfully tested\nthrough bench tests and in vivo tests on a behaving primate to record the local field potential (LFP)\noscillation and stimulate the target area at the same time....
The design of brain-computer interface for the wheelchair for physically disabled people is presented. The design of the proposed\nsystem is based on receiving, processing, and classification of the electroencephalographic (EEG) signals and then performing\nthe control of the wheelchair. The number of experimental measurements of brain activity has been done using human control\ncommands of the wheelchair. Based on the mental activity of the user and the control commands of the wheelchair, the design of\nclassification system based on fuzzy neural networks (FNN) is considered.The design of FNN based algorithm is used for brainactuated\ncontrol. The training data is used to design the system and then test data is applied to measure the performance of the\ncontrol system. The control of the wheelchair is performed under real conditions using direction and speed control commands\nof the wheelchair.The approach used in the paper allows reducing the probability of misclassification and improving the control\naccuracy of the wheelchair....
The idea to use a cost-effective pneumatic padding for sensing of physical interaction\nbetween a user and wearable rehabilitation robots is not new, but until now there has not been\nany practical relevant realization. In this paper, we present a novel method to estimate physical\nhuman-robot interaction using a pneumatic padding based on artificial neural networks (ANNs).\nThis estimation can serve as rough indicator of applied forces/torques by the user and can\nbe applied for visual feedback about the user�s participation or as additional information for\ninteraction controllers. Unlike common mostly very expensive 6-axis force/torque sensors (FTS), the\nproposed sensor system can be easily integrated in the design of physical human-robot interfaces of\nrehabilitation robots and adapts itself to the shape of the individual patient�s extremity by pressure\nchanging in pneumatic chambers, in order to provide a safe physical interaction with high user�s\ncomfort. This paper describes a concept of using ANNs for estimation of interaction forces/torques\nbased on pressure variations of eight customized air-pad chambers. The ANNs were trained one-time\noffline using signals of a high precision FTS which is also used as reference sensor for experimental\nvalidation. Experiments with three different subjects confirm the functionality of the concept and the\nestimation algorithm....
Subjects with amyotrophic lateral sclerosis (ALS) consistently experience decreasing quality\nof life because of this distinctive disease. Thus, a practical brain-computer interface (BCI) application\ncan effectively help subjects with ALS to participate in communication or entertainment. In this\nstudy, a fuzzy tracking and control algorithm is proposed for developing a BCI remote control system.\nTo represent the characteristics of the measured electroencephalography (EEG) signals after visual\nstimulation, a fast Fourier transform is applied to extract the EEG features. A self-developed fuzzy\ntracking algorithm quickly traces the changes of EEG signals. The accuracy and stability of a BCI\nsystem can be greatly improved by using a fuzzy control algorithm. Fifteen subjects were asked to\nattend a performance test of this BCI system. The canonical correlation analysis (CCA) was adopted\nto compare the proposed approach, and the average recognition rates are 96.97% and 94.49% for\nproposed approach and CCA, respectively. The experimental results showed that the proposed\napproach is preferable to CCA. Overall, the proposed fuzzy tracking and control algorithm applied\nin the BCI system can profoundly help subjects with ALS to control air swimmer drone vehicles for\nentertainment purposes....
A quantitative measure of information complexity remains very much desirable in HCI field, since it may\naid in optimization of user interfaces, especially in human-computer systems for controlling complex objects. Our\npaper is dedicated to exploration of subjective (subject-depended) aspect of the complexity, conceptualized as\ninformation familiarity. Although research of familiarity in human cognition and behaviour is done in several fields,\nthe accepted models in HCI, such as Human Processor or Hick-Hyman�s law do not generally consider this issue. In\nour experimental study the subjects performed search and selection of digits and letters, whose familiarity was\nconceptualized as frequency of occurrence in numbers and texts. The analysis showed significant effect of\ninformation familiarity on selection time and throughput in regression models, although the R2 values were somehow\nlow. Still, we hope that our results might aid in quantification of information complexity and its further application\nfor optimizing interaction in human-machine systems....
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