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Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing.

Thielen J, van den Broek P, Farquhar J, Desain P - PLoS ONE (2015)

Bottom Line: We defined a linear generative model that decomposes full responses into overlapping single-flash responses.In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI.These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.

View Article: PubMed Central - PubMed

Affiliation: Radboud University Nijmegen, Donders Center for Cognition, Nijmegen, Netherlands.

ABSTRACT
Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller applications. In this study, we report a novel paradigm for a BBVEP-based BCI that utilizes a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. The linear generative model explains on average 50% and up to 66% of the variance of responses to both seen and unseen sequences. In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI. On average, an online accuracy of 86% was reached with trial lengths of 3.21 seconds. This corresponds to an Information Transfer Rate of 48 bits per minute (approximately 9 symbols per minute). This study indicates the potential to model and predict responses to broad-band stimulation. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.

No MeSH data available.


Pulse responses.The spatially filtered pulse responses derived by the estimation step in reconvolution (left) and corresponding zero-padded power spectra (right) are shown for each participant. The top figures show the pulse responses on a short flash, the bottom ones show those for a long flash. The black bars represent the length of a single flash.
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pone.0133797.g009: Pulse responses.The spatially filtered pulse responses derived by the estimation step in reconvolution (left) and corresponding zero-padded power spectra (right) are shown for each participant. The top figures show the pulse responses on a short flash, the bottom ones show those for a long flash. The black bars represent the length of a single flash.

Mentions: Reconvolution can be split into two steps. The first step is estimation, in which pulse responses are derived from the full response. The two pulse responses that are derived from decomposing the signals according to the structure matrix are shown in Fig 9. These resemble a wavelet-like curve: a modulated sine wave with a constant frequency (between 13 and 15 Hertz) and a participant-dependent phase. Also note that the difference between the two pulse responses indicates an enlargement of the amplitude whenever the underlying impulse gets longer. The amplitude and phase of the pulse responses were not significantly correlated with accuracy.


Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing.

Thielen J, van den Broek P, Farquhar J, Desain P - PLoS ONE (2015)

Pulse responses.The spatially filtered pulse responses derived by the estimation step in reconvolution (left) and corresponding zero-padded power spectra (right) are shown for each participant. The top figures show the pulse responses on a short flash, the bottom ones show those for a long flash. The black bars represent the length of a single flash.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4514763&req=5

pone.0133797.g009: Pulse responses.The spatially filtered pulse responses derived by the estimation step in reconvolution (left) and corresponding zero-padded power spectra (right) are shown for each participant. The top figures show the pulse responses on a short flash, the bottom ones show those for a long flash. The black bars represent the length of a single flash.
Mentions: Reconvolution can be split into two steps. The first step is estimation, in which pulse responses are derived from the full response. The two pulse responses that are derived from decomposing the signals according to the structure matrix are shown in Fig 9. These resemble a wavelet-like curve: a modulated sine wave with a constant frequency (between 13 and 15 Hertz) and a participant-dependent phase. Also note that the difference between the two pulse responses indicates an enlargement of the amplitude whenever the underlying impulse gets longer. The amplitude and phase of the pulse responses were not significantly correlated with accuracy.

Bottom Line: We defined a linear generative model that decomposes full responses into overlapping single-flash responses.In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI.These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.

View Article: PubMed Central - PubMed

Affiliation: Radboud University Nijmegen, Donders Center for Cognition, Nijmegen, Netherlands.

ABSTRACT
Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller applications. In this study, we report a novel paradigm for a BBVEP-based BCI that utilizes a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. The linear generative model explains on average 50% and up to 66% of the variance of responses to both seen and unseen sequences. In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI. On average, an online accuracy of 86% was reached with trial lengths of 3.21 seconds. This corresponds to an Information Transfer Rate of 48 bits per minute (approximately 9 symbols per minute). This study indicates the potential to model and predict responses to broad-band stimulation. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only.

No MeSH data available.