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.

Sequence detection analysis based on canonical correlation for steady-state visual evoked potential brain computer interfaces

“…This strategy significantly increased the classification accuracy compared to PSDA. Moreover, an assisted closed loop (ACL) algorithm was present for optimizing spectrum-based SSVEP recognition (Fernandez-Vargas et al, 2013). Wu and Yao (2008) proposed the stability coefficient (SC) model to improve the performance of SSVEP-based BCIs within a short time window of EEG signals.…”

Adaptive Multiclass Classification for Brain Computer Interfaces

“…This choice is typically experimental and subject dependent and it usually requires some level of prior physiological knowledge. Several algorithms are available for automatically optimizing these parameters Fernandez-Vargas et al, 2013;Ang et al, 2012). In a 1 Authors preprint multi-class BCI problem, the most common feature extractors are based on supervised data projections, such as one-against-one common spatial patterns (CSP) (Blankertz et al, 2008;Dornhege et al, 2004) or multi-class CSP (MCSP) (Grosse-Wentrup et al, 2008).…”

Real-time Hand Motion Reconstruction System for Trans-Humeral Amputees Using EEG and EMG

“…This could be, for example, a digital representation from which the subject can replicate the movement. Second, the use of closed-loop and biofeedback has shown great results in several areas, including BCI (Fernandez-Vargas et al, 2013). Thus, a feedback system should be included in the system to improve the usability and the CV.…”