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. 2022 Apr 28;17(4):e0267548.
doi: 10.1371/journal.pone.0267548. eCollection 2022.

Instant classification for the spatially-coded BCI

Alexander Maÿe  1 , Raika Rauterberg  1   2 , Andreas K Engel  1
Affiliations

Instant classification for the spatially-coded BCI

Alexander Maÿe et al. PLoS One. .

Abstract

The spatially-coded SSVEP BCI exploits changes in the topography of the steady-state visual evoked response to visual flicker stimulation in the extrafoveal field of view. In contrast to frequency-coded SSVEP BCIs, the operator does not gaze into any flickering lights; therefore, this paradigm can reduce visual fatigue. Other advantages include high classification accuracies and a simplified stimulation setup. Previous studies of the paradigm used stimulation intervals of a fixed duration. For frequency-coded SSVEP BCIs, it has been shown that dynamically adjusting the trial duration can increase the system's information transfer rate (ITR). We therefore investigated whether a similar increase could be achieved for spatially-coded BCIs by applying dynamic stopping methods. To this end we introduced a new stopping criterion which combines the likelihood of the classification result and its stability across larger data windows. Whereas the BCI achieved an average ITR of 28.4±6.4 bits/min with fixed intervals, dynamic intervals increased the performance to 81.1±44.4 bits/min. Users were able to maintain performance up to 60 minutes of continuous operation. We suggest that the dynamic response time might have worked as a kind of temporal feedback which allowed operators to optimize their brain signals and compensate fatigue.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Schema of the visual stimulation and the procedure in the training session.
Fig 2
Fig 2. Schema of the visual stimulation and the procedure in the online session.
Fig 3
Fig 3. Flow diagram of the dynamic stopping algorithm.
Fig 4
Fig 4. Effect of the data window size.
A: Classification accuracy. B: ITR.
Fig 5
Fig 5. Online and offline performance (ITR) of the participants.
Bars display the mean ITR across the 10 or more blocks that each participant completed; whiskers show the standard deviation. Asterisks mark differences of the ITRs for the optimal fixed window size and dynamic stopping at the 0.05 significance level (paired Student’s t-test).
Fig 6
Fig 6. Classification accuracy (A) and trial duration (B) for each target.
Fig 7
Fig 7. Relation between SSVEP response magnitude (on the abscissa) and ITR (on the ordinate) for all participants (numbered dots).
Fig 8
Fig 8. ITR change over the online session w.r.t. the first online block for each participant.
Each subject completed a minimum of 10 blocks and continued thereafter at their own discretion.
Fig 9
Fig 9. Effect of filter order on ITR.
Fig 10
Fig 10. Influence of parameters N and P of the dynamic stopping on the ITR.
See this image and copyright information in PMC

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References

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Grants and funding

The work described in this paper was supported by the German research Foundation (DFG www.dfg.de) through project TRR 169/B1. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.