Book chapter
Developing an Optical Brain-Computer Interface for Humanoid Robot Control
Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience, pp 3-13
21 Jun 2016
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This work evaluates the feasibility of a motor imagery-based optical brain-computer interface (BCI) for humanoid robot control. The functional near-infrared spectroscopy (fNIRS) based BCI-robot system developed in this study operates through a high-level control mechanism where user specifies a target action through the BCI and the robot performs the set of micro operations necessary to fulfill the identified goal. For the evaluation of the system, four motor imagery tasks (left hand, right hand, left foot, and right foot) were mapped to operational commands (turn left, turn right, walk forward, walk backward) that were sent to the robot in real time to direct the robot navigating a small room. An ecologically valid offline analysis with minimal preprocessing shows that seven subjects could achieve an average accuracy of 32.5 %. This was increased to 43.6 % just by including calibration data from the same day of the robot control using the same cap setup, indicating that day-of calibration following the initial training may be important for BCI control.
Metrics
Details
- Title
- Developing an Optical Brain-Computer Interface for Humanoid Robot Control
- Creators
- Alyssa M BatulaJesse MarkYoungmoo E KimHasan Ayaz
- Publication Details
- Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience, pp 3-13
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing; Cham
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000456655800001
- Scopus ID
- 2-s2.0-84978924209
- Other Identifier
- 991014877674704721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Cybernetics
- Ergonomics
- Neurosciences
- Psychology, Experimental