Frequency Superposition – A Multi-Frequency Stimulation Method in SSVEP-based BCIs

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

doi:10.1109/embc46164.2021.9630511

Cited by 9 publications

(18 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In multi-frequency SSVEP, the resulting brain response shows not only the input frequencies and their harmonics, but also the interactions between the input frequencies, in the form of the integer linear combinations of the input frequencies (Mu et al, 2021a ). These peak occurrences in the SSVEP response increase the chances of the common sums as described above.…”

Section: Methodsmentioning

confidence: 99%

“…In this work, we have chosen frequency superposition (Mu et al, 2021a ) as the multi-frequency stimulation method for its simplicity. Multi-frequency SSVEP stimulated with frequency superposition contains not only the input frequencies and their harmonics, but also the integer linear combinations of the input frequencies, with lower order interactions are more likely to be observed (Mu et al, 2021a , b ).…”

Section: Methodsmentioning

confidence: 99%

“…To address this problem, multi-frequency stimulation methods utilizing more than one frequency in each stimulus have been introduced, with two frequencies (dual-frequency) being the most widely used modality (Shyu et al, 2010 ; Zhang et al, 2012 ; Chen et al, 2013 ; Hwang et al, 2013 ; Kimura et al, 2013 ; Chang et al, 2014 ; Mu et al, 2021a ). However, these studies focused primarily on introducing multi-frequency stimulation methods, and did not explore frequency selection methods.…”

Section: Introductionmentioning

confidence: 99%

“…Multi-frequency stimulation generates a complex periodic stimulation signal that triggers a more complex SSVEP response. In Mu et al ( 2021a ), it was demonstrated that multi-frequency SSVEP response contained not only the input frequencies and their harmonics, but also the integer linear combinations of the input frequencies with low order interactions more likely to be observed in the recorded SSVEP. Note that the order of interaction was defined as the sum of absolute values of the coefficients in the linear combination (Mu et al, 2021a ).…”

Section: Introductionmentioning

confidence: 99%

“…In Mu et al ( 2021a ), it was demonstrated that multi-frequency SSVEP response contained not only the input frequencies and their harmonics, but also the integer linear combinations of the input frequencies with low order interactions more likely to be observed in the recorded SSVEP. Note that the order of interaction was defined as the sum of absolute values of the coefficients in the linear combination (Mu et al, 2021a ). A dedicated decoding algorithm, multi-frequency canonical correlation analysis (MFCCA), was also introduced to decode complex multi-frequency SSVEP, leveraging the linear combinations produced by the frequency interactions (Mu et al, 2021b ).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Grayden

Tan

et al. 2022

Front. Neurosci.

View full text Add to dashboard Cite

ObjectiveMulti-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.MethodsAn 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.ResultsOur 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.ConclusionPerforming 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.SignificanceThis 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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Grayden

Tan

et al. 2022

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

Linear Diophantine equation (LDE) decoder: A training‐free decoding algorithm for multifrequency SSVEP with reduced computation cost

Tan

Grayden

et al. 2023

Asian Journal of Control

Self Cite

View full text Add to dashboard Cite

Multifrequency steady‐state visual evoked potentials (SSVEPs) have been developed to extend the capability of SSVEP‐based brain‐machine interfaces (BMIs) to complex applications that have large numbers of targets. Even though various multifrequency stimulation methods have been introduced, the decoding algorithms for multifrequency SSVEP are still in early development. The recently developed multifrequency canonical correlation analysis (MFCCA) was shown to be a feasible training‐free option to use in decoding multifrequency SSVEPs. However, the time complexity of MFCCA is shown to be , which will lead to long computation time as grows, where represents the input size in decoding. In this paper, a novel decoding algorithm is proposed with the aim to reduce the time complexity. This algorithm is based on linear Diophantine equation solvers and has a reduced computation cost while remaining training‐free. Our simulation results demonstrated that linear Diophantine equation (LDE) decoder run time is only one fifth of MFCCA run time under respective optimal settings on 5‐s single‐channel data. This reduced computation cost makes it easier to implement multifrequency SSVEP in real‐time systems. The effectiveness of this new decoding algorithm is validated with nine healthy participants when using dry electrode scalp electroencephalography (EEG).

show abstract

Multi-frequency steady-state visual evoked potential dataset

Mu,

Liu,

Burkitt

et al. 2024

Sci Data

View full text Add to dashboard Cite

The Steady-State Visual Evoked Potential (SSVEP) is a widely used modality in Brain-Computer Interfaces (BCIs). Existing research has demonstrated the capabilities of SSVEP that use single frequencies for each target in various applications with relatively small numbers of commands required in the BCI. Multi-frequency SSVEP has been developed to extend the capability of single-frequency SSVEP to tasks that involve large numbers of commands. However, the development on multi-frequency SSVEP methodologies is falling behind compared to the number of studies with single-frequency SSVEP. This dataset was constructed to promote research in multi-frequency SSVEP by making SSVEP signals collected with different frequency stimulation settings publicly available. In this dataset, SSVEPs were collected from 35 participants using single-, dual-, and tri-frequency stimulation and with three different multi-frequency stimulation variants.

show abstract

Frequency Superposition – A Multi-Frequency Stimulation Method in SSVEP-based BCIs

Cited by 9 publications

References 15 publications

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

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

Linear Diophantine equation (LDE) decoder: A training‐free decoding algorithm for multifrequency SSVEP with reduced computation cost

Multi-frequency steady-state visual evoked potential dataset

Contact Info

Product

Resources

About