While recent research on Brain-Computer Interfaces (BCI) has highlighted their potential for many applications, they remain barely used outside laboratories. The main reason is their lack of robustness. Indeed, with current BCI, mental state recognition is usually slow and often incorrect. Spontaneous BCI (i.e., mental imagery-based BCI) often rely on mutual learning efforts by the user and the...

Co-adaptive training paradigms for event-related desynchronization (ERD) based brain-computer interfaces (BCI) have proven effective for healthy users. As of yet, it is not clear whether co-adaptive training paradigms can also benefit users with severe motor impairment. The primary goal of our paper was to evaluate a novel cue-guided, co-adaptive BCI training paradigm with severely impaired vol...

Brain-Computer Interface (BCI) research and (future) applications raise important ethical issues that need to be addressed to promote societal acceptance and adequate policies. Here we report on a survey we conducted among 145 BCI researchers at the 4th International BCI conference, which took place in May-June 2010 in Asilomar, California. We assessed respondents' opinions about a number of to...

Due to the non-stationarity of EEG signals, online training and adaptation is essential to EEG based brain-computer interface (BCI) systems. Asynchronous BCI offers more natural human-machine interaction, but it is a great challenge to train and adapt an asynchronous BCI online because the user’s control intention and timing are usually unknown. This paper proposes a novel motor imagery based a...

(E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI) is known as a dual specific phosphatase 1/6 or MAPK inhibitor. However, its precise anti-lung cancer mechanism remains unknown. In this study, the effects of BCI on cell viability were investigated in the non-small cell lung cancer cell lines NCIH1299, A549, and NCI-H460. We confirmed that BCI significantly inhibited the cel...

Recently, spatio-temporal filtering to enhance decoding for Brain-Computer-Interfacing (BCI) has become increasingly popular. In this work, we discuss a novel, fully Bayesian-and thereby probabilistic-framework, called Bayesian Spatio-Spectral Filter Optimization (BSSFO) and apply it to a large data set of 80 non-invasive EEG-based BCI experiments. Across the full frequency range, the BSSFO fra...

The scope of this chapter is limited to applications where a Brain–Computer Interface (BCI) is used as an explicit interaction technique. In other words, we refer here to BCI as input which is voluntarily controlled by the user, rather than as an implicit interaction as in for mental or cognitive state monitoring. Designing applications using BCI as an explicit input technique for users with se...

OBJECTIVE Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. METHODS We hypothesized that integrity and connectivity of involved whi...