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A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility.
Brain–computer interfaces (BCI) based on event-related potentials (ERP) allow for selection of characters from a visually presented character-matrix and thus provide a communica- tion channel for users with neurodegenerative disease. Although they have been topic of research for more than 20 years and were multiply proven to be a reliable communication method, BCIs are almost exclusively used in experimental settings, handled by qualified experts. This study investigates if ERP–BCIs can be handled independently by laymen without expert support, which is inevitable for establishing BCIs in end-user’s daily life situations. Furthermore we compared the classic character-by-character text entry against a predictive text entry (PTE) that directly incorporates predictive text into the character- matrix. N = 19 BCI novices handled a user-centered ERP–BCI application on their own without expert support. The software individually adjusted classifier weights and control parameters in the background, invisible to the user (auto-calibration). All participants were able to operate the software on their own and to twice correctly spell a sentence with the auto-calibrated classifier (once with PTE, once without). Our PTE increased spelling speed and, importantly, did not reduce accuracy. In sum, this study demonstrates feasi- bility of auto-calibrating ERP–BCI use, independently by laymen and the strong benefit of integrating predictive text directly into the character-matrix.
Background: A solid diagnosis of sleep disorders in children should include both self-ratings and parent ratings. However, there are few standardized self-assessment instruments to meet this need. The Children’s Sleep Comic is an adapted version of the unpublished German questionnaire “Freiburger Kinderschlafcomic” and provides pictures for items and responses. Because the drawings were outdated and allowed only for qualitative analysis, we revised the comic, tested its applicability in a target sample, and suggest a procedure for quantitative analysis. Methods: All items were updated and pictures were newly drawn. We used a sample of 201 children aged 5–10 years to test the applicability of the Children’s Sleep Comic in young children and to run a preliminary analysis. Results: The Children’s Sleep Comic comprises 37 items covering relevant aspects of sleep disorders in children. Application took on average 30 minutes. The procedure was well accepted by the children, as reflected by the absence of any dropouts. First comparisons with established questionnaires indicated moderate correlations. Conclusion: The Children’s Sleep Comic is appropriate for screening sleep behavior and sleep problems in children. The interactive procedure can foster a good relationship between the investigator and the child, and thus establish the basis for successful intervention if necessary.