2022
DOI: 10.1038/s41598-022-18088-w
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Identifying potential training factors in a vibrotactile P300-BCI

Abstract: Brain–computer interfaces (BCI) often rely on visual stimulation and feedback. Potential end-users with impaired vision, however, cannot use these BCIs efficiently and require a non-visual alternative. Both auditory and tactile paradigms have been developed but are often not sufficiently fast or accurate. Thus, it is particularly relevant to investigate if and how users can train and improve performance. We report data from 29 healthy participants who trained with a 4-choice tactile P300-BCI during five sessio… Show more

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Cited by 6 publications
(12 citation statements)
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References 70 publications
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“…As in the cEEGrid data, ERP polarity of P37 was inversed. Eidel et al 10.3389/fnhum.2024.1371631 Frontiers in Human Neuroscience 10 frontiersin.org study (74.9% in session one, Eidel and Kübler, 2022). In contrast to the results of the prior study, however, we found no significant correlation between the somatosensory sensitivities and (offline) accuracies.…”
Section: Somatosensory Sensitivity (H4)contrasting
confidence: 99%
See 3 more Smart Citations
“…As in the cEEGrid data, ERP polarity of P37 was inversed. Eidel et al 10.3389/fnhum.2024.1371631 Frontiers in Human Neuroscience 10 frontiersin.org study (74.9% in session one, Eidel and Kübler, 2022). In contrast to the results of the prior study, however, we found no significant correlation between the somatosensory sensitivities and (offline) accuracies.…”
Section: Somatosensory Sensitivity (H4)contrasting
confidence: 99%
“…Table 2 provides an overview of the determined sensitivity thresholds per participant. As in our last study (Eidel and Kübler, 2022), some participants quickly revealed a distinct threshold, whereas the responses of other participants were less consistent, necessitating more trials (on average, 37 ± 8.7 trials were applied). The equal/unequal responses from P20 were still too inconsistent to calculate a threshold, despite performing 53 trials, and were excluded from the analysis.…”
Section: Intensity Discriminationsupporting
confidence: 61%
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“…However, in contrast to protocols with continuous feedback, ERP-based protocols would require intermittent feedback on the basis of specific trials, which overall implies that less frequent feedback is provided. Nevertheless, a learning curve for increasing ERP amplitudes after training has been demonstrated in the field of brain-computer interfacing (BCI, Eidel and Kübler, 2022; Ziebell et al, 2020) and, in general, observations from BCI research may have great potential to be transferred to the domain of FMT-neurofeedback, as already suggested by Jeunet et al (2018). Specifically, they propose the use of adaptive thresholds, machine learning tools for customizing neurofeedback features, applying performance predictors for selecting suitable neurofeedback protocols, integrating tactile and computerized social and emotional feedback to boost motivation, and the identification of optimal mental strategies through algorithms initially developed for BCI to enhance individual training outcomes.…”
Section: Discussionmentioning
confidence: 99%