Nonstationary brain source separation for multiclass motor imagery.
This paper describes a method to recover task-related brain sources in the context of multiclass brain--computer interfaces (BCIs) based on noninvasive EEG. We extend the method joint approximate diagonalization (JAD) for spatial filtering using a maximum likelihood framework. This generic formulation: 1) bridges the gap between the common spatial patterns (CSPs) and blind source separation of nonstationary sources; and 2) leads to a neurophysiologically adapted version of JAD, accounting for the successive activations/deactivations of brain sources during motor imagery (MI) trials. Using dataset 2a of BCI Competition IV (2008) in which nine subjects were involved in a four-class two-session MI-based BCI experiment, a quantitative evaluation of our extension is provided by comparing its performance against JAD and CSP in the case of cross-validation, as well as session-to-session transfer. While JAD, as already proposed in other works, does not prove to be significantly better than classical one-versus-rest CSP, our extension is shown to perform significantly better than CSP for cross-validated and session-to-session performance. The extension of JAD introduced in this paper yields among the best session-to-session transfer results presented so far for this particular dataset; thus, it appears to be of great interest for real-life BCIs.
Department Images-Signal, Grenoble Images, Speech, Signal and Control Laboratory, Grenoble 38031, France. cedric.gouypailler@gmail.com
Cédric
C
Gouy-Pailler
Marco
M
Congedo
Clemens
C
Brunner
Christian
C
Jutten
Gert
G
Pfurtscheller
D000465:Algorithms; D000704:Analysis of Variance; D001921:Brain / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008328:Man-Machine Systems; D009043:Motor Activity; D011597:Psychomotor Performance; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted