Authors:
Hendrik Woehrle
1
and
Elsa Andrea Kirchner
2
Affiliations:
1
German Research Center for Artificial Intelligence (DFKI GmbH), Germany
;
2
German Research Center for Artificial Intelligence (DFKI GmbH) and University of Bremen, Germany
Keyword(s):
Brain Computer Interfaces, Embedded Brain Reading, P300, Single Trial Detection, Exoskeleton.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Assistive Technologies
;
Biomedical Devices for Computer Interaction
;
Biomedical Engineering
;
Biomedical Instruments and Devices
;
Biomedical Signal Processing
;
Biosignal Acquisition, Analysis and Processing
;
Brain-Computer Interfaces
;
Collaboration and e-Services
;
Data Manipulation
;
Devices
;
e-Business
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Rehabilitation
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Physiology-Driven Computer Interaction
;
Sensor Networks
;
Soft Computing
;
Software Engineering
;
Usability
;
Usability and Ergonomics
;
Wearable Sensors and Systems
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
Abstract:
The detection of event related potentials and their usage for innovative tasks became a mature research topic
in the last couple of years for brain computer interfaces. However, the typical experimental setups are usually
highly controlled and designed to actively evoke specific brain activity like the P300 event related potential. In
this paper, we show that the detection and passive usage of the P300 related brain activity is possible in highly
uncontrolled and noisy application scenarios where the subjects are performing demanding senso-motor task,
i.e., telemanipulation of a real robotic arm. In the application scenario, the subject wears an exoskeleton to
control a robotic arm, which is presented to him in a virtual scenario. While performing the telemanipulation
task he has to respond to important messages. By online analysis of the subject’s electroencephalogram we
detect P300 related target recognition processes to infer on upcoming response behavior or missing of response
be
havior in case a target was not recognized. We show that a classifier that is trained to distinguish between
brain activity evoked by recognized task relevant stimuli and ignored frequent task irrelevant stimuli can be
applied to classify between brain activity evoked by recognized task relevant stimuli and brain activity that is
evoked in case that task relevant stimuli are not recognized.
(More)