title: Assisted closed-loop optimization of SSVEP-BCI efficiency.
abstract: We designed a novel assisted closed-loop optimization protocol to improve the efficiency of brain-computer interfaces (BCI) based on steady state visually evoked potentials (SSVEP). In traditional paradigms, the control over the BCI-performance completely depends on the subjects' ability to learn from the given feedback cues. By contrast, in the proposed protocol both the subject and the machine share information and control over the BCI goal. Generally, the innovative assistance consists in the delivery of online information together with the online adaptation of BCI stimuli properties. In our case, this adaptive optimization process is realized by (1) a closed-loop search for the best set of SSVEP flicker frequencies and (2) feedback of actual SSVEP magnitudes to both the subject and the machine. These closed-loop interactions between subject and machine are evaluated in real-time by continuous measurement of their efficiencies, which are used as online criteria to adapt the BCI control parameters. The proposed protocol aims to compensate for variability in possibly unknown subjects' state and trait dimensions. In a study with N = 18 subjects, we found significant evidence that our protocol outperformed classic SSVEP-BCI control paradigms. Evidence is presented that it takes indeed into account interindividual variabilities: e.g., under the new protocol, baseline resting state EEG measures predict subjects' BCI performances. This paper illustrates the promising potential of assisted closed-loop protocols in BCI systems. Probably their applicability might be expanded to innovative uses, e.g., as possible new diagnostic/therapeutic tools for clinical contexts and as new paradigms for basic research.
authors: Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid Madrid, Spain.
authors: Jacobo
authors: J
authors: Fernandez-Vargas
authors: Hanns U
authors: HU
authors: Pfaff
authors: Francisco B
authors: FB
authors: Rodríguez
authors: Pablo
authors: P
authors: Varona
mesh_terms: D000293:Adolescent; D000328:Adult; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010775:Photic Stimulation / Q000379:methods*; D011597:Psychomotor Performance / Q000502:physiology*; D064871:Teach-Back Communication / Q000379:methods*; D055815:Young Adult
keywords: BCI illiteracy; BCI performance predictor; activity-dependent stimulation; brain-computer interface; brain-machine interface; individual alpha frequency; resting state EEG; resting state network