Integrating human and computer vision with EEG toward the control of a prosthetic arm

We are undertaking the development of a brain computer interface (BCI) [1] for control of an upper limb prosthetic. Our approach exploits electrical neural activity data for motor intent estimation, and eye gaze direction for target selection. These data streams are augmented by computer vision (CV)...

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Bibliographic Details
Published in:2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI) pp. 179 - 180
Main Authors: Lavely, Eugene, Meltzner, Geoffrey, Thompson, Rick
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 05.03.2012
IEEE
Series:ACM Conferences
Subjects:
Computer systems organization > Embedded and cyber-physical systems > Robotics > Robotic components
Computer vision
EEG
Electroencephalography
Estimation
Humans
neuroprosthetic
Prosthetics
Robot sensing systems
robotic control
robotic control
neuroprosthetic
eeg
computer vision
ISBN:145031063X, 9781450310635
ISSN:2167-2121
Online Access:Get full text
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Summary:We are undertaking the development of a brain computer interface (BCI) [1] for control of an upper limb prosthetic. Our approach exploits electrical neural activity data for motor intent estimation, and eye gaze direction for target selection. These data streams are augmented by computer vision (CV) for 3D scene reconstruction, and are integrated with a hierarchical controller to achieve semi-autonomous control. User interfaces for the effective control of the many degrees of freedom (DOF) of advanced prosthetic arms are not yet available [2]. Ideally the combined arm and interface technology provides the user with reliable and dexterous capability for reaching, grasping and fine-scale manipulation. Technologies that improve arm embodiment i.e., the impression by the amputee subject that the arm is a natural part of their body-concept presents an important and difficult challenge to the human-robot interaction research community. Such embodiment is clearly predicated on cross-disciplinary advances, including accurate intent estimation and an and an algorithmic basis for natural arm control.
ISBN:145031063X
9781450310635
ISSN:2167-2121
DOI:10.1145/2157689.2157744