Paper Info
Title
The Berlin Brain-Computer Interface: Machine Learning Based Detection of User Specific Brain States
Abstract
We outline the Berlin Brain-Computer Interface (BBCI), a system which enables us to translate brain signals from movements or movement intentions into control commands. The main contribution of the BBCI, which is a non-invasive EEG-based BCI system, is the use of advanced machine learning techniques that allow to adapt to the specific brain signatures of each user with literally no training. In BBCI a calibration session of about 20min is necessary to provide a data basis from which the individualized brain signatures are inferred. This is very much in contrast to conventional BCI approaches that rely on operand conditioning and need extensive subject training of the order 50-100 hours. Our machine learning concept thus allows to achieve high quality feedback already after the very first session. This work reviews a broad range of investigations and experiments that have been performed within the BBCI project. In addition to these general paradigmatic BCI results, this work provides a condensed outline of the underlying machine learning and signal processing techniques that make the BBCI succeed. In the first experimental paradigm we analyze the predictability of limb movement long before the actual movement takes place using only the movement intention measured from the pre-movement (readiness) EEG potentials. The experiments include both off-line studies and an online feedback paradigm. The limits with respect to the spatial resolution of the somatotopy are explored by contrasting brain patterns of movements of left vs. right hand rsp. foot. In a second complementary paradigm voluntary modulations of sensorimotor rhythms caused by motor imagery (left hand vs. right hand vs. foot) are translated into a continuous feedback signal. Here we report results of a recent feedback study with 6 healthy subjects with no or very little experience with BCI control: half of the subjects achieved an information transfer rate above 35 bits per minute (bpm). Furthermore one subject used the BBCI to operate a mental typewriter in free spelling mode. The overall spelling speed was 4.5-8 letters per minute including the time needed for the correction errors.
Year
Venue
Keywords
2006
JOURNAL OF UNIVERSAL COMPUTER SCIENCE
brain-computer interface,classification,common spatial patterns,EEG,ERD,event-related desynchronization,feedback,information transfer rate,readiness potential,RP,machine learning,single-trial analysis
Field
DocType
Volume
Little finger,Information transfer,Computer science,Brain–computer interface,Traumatic amputations,Speech recognition,Spelling,Artificial intelligence,Rhythm,Machine learning,Electroencephalography,Motor imagery
Journal
12
Issue
ISSN
Citations 
6
0948-695X
32
PageRank 
References 
Authors
6.56
11
6
Name
Order
Citations
PageRank
Benjamin Blankertz1326.56
Guido Dornhege259684.14
Steven Lemm3100680.98
Matthias Krauledat424428.14
Gabriel Curio51220201.67
Klaus-Robert Müller6127561615.17