منابع مشابه
Bimodal brain-machine interface for motor control of robotic prosthetic
We are working on mapping multi-channel neural spike data, recordedfrom multiple cortical areas ofan owl monkey, to corresponding 3d monkey arm positions. In earlier work on this mapping task, we observed that continuous function approximotors (such as artificial neural networks) have diflculty in jointly estimating 36 arm positions for two distinct cases namely, when the monkey’s arm is statio...
متن کاملMicroelectrode Brain-machine Interface
INTRODUCTION Spinal cord injury (SCI) is a debilitating condition that affects over 250,000 people in the United States [1]. It results in paraplegia (paralysis of the lower limbs) or in tetraplegia (paralysis of the body below the neck) depending on where along the spinal column is affected [2]. It can result from either a physical injury to the head or spine or can be caused by a degenerative...
متن کاملA Study of Various Feature Extraction Methods on a Motor Imagery Based Brain Computer Interface System
Introduction: Brain Computer Interface (BCI) systems based on Movement Imagination (MI) are widely used in recent decades. Separate feature extraction methods are employed in the MI data sets and classified in Virtual Reality (VR) environments for real-time applications. Methods: This study applied wide variety of features on the recorded data using Linear Discriminant Analysis (LDA) classifie...
متن کاملBrain Computer Interface using Machine Learning
This paper presents the design and development of a complete hardware and software solution for a brain computer interface (BCI). It consists of a non-intrusive multiple channel data acquisition device which captures the electrical brain wave signals and passes the data to a computer. The computer then uses signal processing and machine learning algorithms to identify patterns in the signals re...
متن کاملBrain-machine interface for eye movements.
A number of studies in tetraplegic humans and healthy nonhuman primates (NHPs) have shown that neuronal activity from reach-related cortical areas can be used to predict reach intentions using brain-machine interfaces (BMIs) and therefore assist tetraplegic patients by controlling external devices (e.g., robotic limbs and computer cursors). However, to our knowledge, there have been no studies ...
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ژورنال
عنوان ژورنال: The Brain & Neural Networks
سال: 2012
ISSN: 1340-766X,1883-0455
DOI: 10.3902/jnns.19.112