Frequency set selection for multi-frequency steady-state visual evoked potential-based brain-computer interfaces

Jing Mu; David B Grayden; Ying Tan; Denny Oetomo

doi:10.3389/fnins.2022.1057010

Frequency set selection for multi-frequency steady-state visual evoked potential-based brain-computer interfaces

Front Neurosci. 2022 Dec 21:16:1057010. doi: 10.3389/fnins.2022.1057010. eCollection 2022.

Authors

Jing Mu¹, David B Grayden^{2

3}, Ying Tan^{1

3}, Denny Oetomo^{1

3}

Affiliations

¹ Department of Mechanical Engineering, The University of Melbourne, Parkville, VIC, Australia.
² Department of Biomedical Engineering, The University of Melbourne, Parkville, VIC, Australia.
³ Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville, VIC, Australia.

Abstract

Objective: Multi-frequency steady-state visual evoked potential (SSVEP) stimulation and decoding methods enable the representation of a large number of visual targets in brain-computer interfaces (BCIs). However, unlike traditional single-frequency SSVEP, multi-frequency SSVEP is not yet widely used. One of the key reasons is that the redundancy in the input options requires an additional selection process to define an effective set of frequencies for the interface. This study investigates systematic frequency set selection methods.

Methods: An optimization strategy based on the analysis of the frequency components in the resulting multi-frequency SSVEP is proposed, investigated and compared to existing methods, which are constructed based on the analysis of the stimulation (input) signals. We hypothesized that minimizing the occurrence of common sums in the multi-frequency SSVEP improves the performance of the interface, and that selection by pairs further increases the accuracy compared to selection by frequencies. An experiment with 12 participants was conducted to validate the hypotheses.

Results: Our results demonstrated a statistically significant improvement in decoding accuracy with the proposed optimization strategy based on multi-frequency SSVEP features compared to conventional techniques. Both hypotheses were validated by the experiments.

Conclusion: Performing selection by pairs and minimizing the number of common sums in selection by pairs are effective ways to select suitable frequency sets that improve multi-frequency SSVEP-based BCI accuracies.

Significance: This study provides guidance on frequency set selection in multi-frequency SSVEP. The proposed method in this study shows significant improvement in BCI performance (decoding accuracy) compared to existing methods in the literature.

Keywords: brain-computer interface (BCI); brain-machine interface (BMI); dual-frequency; electroencephalography (EEG); multi-frequency; optimization; steady-state visual evoked potential (SSVEP).