title: EEG error potentials detection and classification using time-frequency features for robot reinforcement learning.
abstract: In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials (SSVEP) techniques, ErrP, which appear when a classification error occurs, are not easily recognizable by only examining the temporal or frequency characteristics of EEG signals. A supplementary classification process is therefore needed to identify them in order to stop the course of the action and back up to a recovery state. This paper presents a set of time-frequency (t-f) features for the detection and classification of EEG ErrP in extra-brain activities due to misclassification observed by a user exploiting non-invasive BMI and robot control in the task space. The proposed features are able to characterize and detect ErrP activities in the t-f domain. These features are derived from the information embedded in the t-f representation of EEG signals, and include the Instantaneous Frequency (IF), t-f information complexity, SVD information, energy concentration and sub-bands' energies. The experiment results on real EEG data show that the use of the proposed t-f features for detecting and classifying EEG ErrP achieved an overall classification accuracy up to 97% for 50 EEG segments using 2-class SVM classifier.
authors.forename: Larbi
authors.initials: L
authors.lastname: Boubchir
authors.forename: Youcef
authors.initials: Y
authors.lastname: Touati
authors.forename: Boubaker
authors.initials: B
authors.lastname: Daachi
authors.forename: Arab Ali
authors.initials: AA
authors.lastname: Chérif
mesh_terms: D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual; D006801:Humans; D008962:Models, Theoretical; D012371:Robotics / Q000295:instrumentation*