Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms.
Noninvasive electroencephalogram (EEG) recordings provide for easy and safe access to human neocortical processes which can be exploited for a brain-computer interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. We systematically analyze and develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: 1) the combination of classifiers, each specifically tailored for different physiological phenomena, e.g., slow cortical potential shifts, such as the pre-movement Bereitschaftspotential or differences in spatio-spectral distributions of brain activity (i.e., focal event-related desynchronizations) and 2) behavioral paradigms inducing the subjects to generate one out of several brain states (multiclass approach) which all bare a distinctive spatio-temporal signature well discriminable in the standard scalp EEG. We derive information-theoretic predictions and demonstrate their relevance in experimental data. We will show that a suitably arranged interaction between these concepts can significantly boost BCI performances.
Dornhege
Guido
G
Fraunhofer FIRST (IDA), 12489 Berlin, Germany. guido.dornhege@first.fraunhofer.de
Blankertz
Benjamin
B
Curio
Gabriel
G
Müller
Klaus-Robert
KR
D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009044:Motor Cortex / Q000502:physiology*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity