Brain-computer interface: a new communication device for handicapped persons
Journal of Microcomputer Applications - Special issue on computer applications for handicapped persons
Hidden Markov models for online classification of single trial EEG data
Pattern Recognition Letters
Causal architecture, complexity and self-organization in time series and cellular automata
Causal architecture, complexity and self-organization in time series and cellular automata
BOINC: A System for Public-Resource Computing and Storage
GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
Joint time-frequency-space classification of EEG in a brain-computer interface application
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
The computational structure of spike trains
Neural Computation
Presence: Teleoperators and Virtual Environments
Hi-index | 0.01 |
In this paper, we introduce two new features for the design of electroencephalography (EEG) based Brain-Computer Interfaces (BCI): one feature based on multifractal cumulants and the other feature based on the predictive complexity of the EEG time series. The multifractal cumulants feature measures the signal regularity, while the predictive complexity measures the difficulty to predict the future of the signal based on its past, hence a degree of how complex it is. We have conducted an evaluation of the performance of these two novel features on EEG data corresponding to motor-imagery. We also compared them to the gold standard features used in the BCI field, namely the band-power features. We evaluated these three kinds of features and their combinations on EEG signals from 13 subjects. Results obtained show that our novel features can lead to BCI designs with improved classification performance, notably when using and combining the three kinds of feature (band-power, multifractal cumulants, predictive complexity) together.