Authors:
Mario Michael Krell
1
;
Hendrik Wöhrle
2
and
Anett Seeland
2
Affiliations:
1
University of Bremen, Germany
;
2
Robotics Innovation Center and German Research Center for Artificial Intelligence GmbH, Germany
Keyword(s):
xDAWN, Spatial Filtering, Online Learning, Electroencephalogram, Event-Related Potential, Brain-Computer Interface
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Instruments and Devices
;
Biomedical Signal Processing
;
Brain-Computer Interfaces
;
Data Manipulation
;
Devices
;
EMG Signal Processing and Applications
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
NeuroSensing and Diagnosis
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
The xDAWN algorithm is a well-established spatial filter which was developed to enhance the signal quality of brain-computer interfaces for the detection of event-related potentials. Recently, an adaptive version has been introduced. Here, we present an improved version that incorporates regularization to reduce the influence of noise and avoid overfitting. We show that regularization improves the performance significantly for up to 4%, when little data is available as it is the case when the brain-computer interface should be used without or with a very short prior calibration session.