We propose a strategy, developed from sequential analysis, to vary the window-length used for classification. Our proposed technique adapts to the data, ...

In order to classify which of several frequencies a user is attending to, these classifiers wait for a fixed length of input EEG data before making a decision.

A strategy, developed from sequential analysis, to vary the window-length used for classification for SSVEP-based BCIs, which improves the classifier ...

May 14, 2013 · The proposed technique adapts to the data, continuing to collect data until it is confident enough to make a classification decision. The ...

Oct 22, 2024 · Our proposed technique adapts to the data, continuing to collect data until it is confident enough to make a classification decision. Our ...

A key problem for SSVEP-based BCIs is to classify which modulation frequency the user is attending, for which there is an inherent trade-off between speed and ...

We present a dynamic window-length classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs)

Sep 5, 2016 · Item Detail ; Language, English ; PubMed ID, 24111371 ; Title, Sequential selection of window length for improved SSVEP-based BCI classification.

Bibliographic details on Sequential selection of window length for improved SSVEP-based BCI classification.

We present a dynamic window-length classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that does not require ...