An In-depth Study of SSVEP Signals Against Stimulus Frequency and Distance to the Stimulus

Pathiranage, Sachinthana; Paranawithana, Ishara; Perera, M.Y.A.; Silva, Anjula C. De

doi:10.1109/mercon.2018.8421897

Cited by 2 publications

(1 citation statement)

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“…However, for the 10, 909 Hz frequency, the ITR experiences a decrease for the specific cases in which the MEC and CCA methods are applied in the CBR paradigm since, although the detection times remain at the minimum values compared to the ratings associated with the other visual paradigms, the accuracy decreases and consequently the ITR decreases. For the frequencies of 15, 20, and 24 Hz the detection times reach the global maximum values with the CBR paradigm, and at the same time, with the increase of the stimulation frequencies, the accuracy decreases (Regan, 1989 ; Pathiranage et al, 2018 ), even below the chance level at 20 and 24 Hz, which is why the ITR becomes null for the MEC detection method, which also applies to the CBS paradigm. Based on the results obtained, we can summarize that the FBCCA method outperforms the other two detection methods and that the best overall performances are obtained by combining this SSVEP detection method with the OOR or OOS paradigms.…”

Section: Discussionmentioning

confidence: 99%

SSVEP detection assessment by combining visual stimuli paradigms and no-training detection methods

et al. 2023

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IntroductionBrain-Computer Interfaces (BCI) based on Steady-State Visually Evoked Potentials (SSVEP) have great potential for use in communication applications because of their relatively simple assembly and in some cases the possibility of bypassing the time-consuming training stage. However, among multiple factors, the efficient performance of this technology is highly dependent on the stimulation paradigm applied in combination with the SSVEP detection algorithm employed. This paper proposes the performance assessment of the classification of target events with respect to non-target events by applying four types of visual paradigms, rectangular modulated On-Off (OOR), sinusoidal modulated On-Off (OOS), rectangular modulated Checkerboard (CBR), and sinusoidal modulated Checkerboard (CBS), with three types of SSVEP detection methods, Canonical Correlation Analysis (CCA), Filter-Bank CCA (FBCCA), and Minimum Energy Combination (MEC).MethodsWe set up an experimental protocol in which the four types of visual stimuli were presented randomly to twenty-seven participants and after acquiring their electroencephalographic responses to five stimulation frequencies (8.57, 10.909, 15, 20, and 24 Hz), the three detection methods were applied to the collected data.ResultsThe results are conclusive, obtaining the best performance with the combination of either OOR or OOS visual stimulus and the FBCCA as a detection method, however, this finding contrasts with the opinion of almost half of the participants in terms of visual comfort, where the 51.9% of the subjects felt more comfortable and focused with CBR or CBS stimulation.DiscussionFinally, the EEG recordings correspond to the SSVEP response of 27 subjects to four visual paradigms when selecting five items on a screen, which is useful in BCI navigation applications. The dataset is available to anyone interested in studying and evaluating signal processing and machine-learning algorithms for SSVEP-BCI systems.

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Section: Discussionmentioning

confidence: 99%