In this paper we identify developments that have led to the current interest from computer scientists in Brain-Computer Interfacing (BCI). Nondisabled users have become a target group for BCI applications. Non-disabled users can not be treated as patients. They are free to move and use their hands during the interaction with an application. Therefore BCI should be integrated in a multimodal app...

It is widely accepted in the brain computer interface (BCI) research community that neurological phenomena are the only source of control in any BCI system. Artifacts are undesirable signals that can interfere with neurological phenomena. They may change the characteristics of neurological phenomena or even be mistakenly used as the source of control in BCI systems. Electrooculography (EOG) and...

A new Brain-Computer Interface (BCI) technique, which is called a hybrid BCI, has recently been proposed to address the limitations of conventional single BCI system. Although some hybrid BCI studies have shown promising results, the field of hybrid BCI is still in its infancy and there is much to be done. Especially, since the hybrid BCI systems are so complicated and complex, it is difficult ...

Paralysis after stroke or neurotrauma is among the leading causes of long term disability in adults. The development of brain-computer-interface (BCI) systems that allow online classification of electric or metabolic brain activity and their translation into control signals of external devices or computers have led to two major approaches in tackling the problem of paralysis. While assistive BC...

While research on brain-computer interfacing (BCI) has seen tremendous progress in recent years, performance still varies substantially between as well as within subjects, with roughly 10 20% of subjects being incapable of successfully operating a BCI system. In this short report, I argue that this variation in performance constitutes one of the major obstacles that impedes a successful commerc...

Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10-50%) of subjects are BCI-inefficient users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve...

Objective: Brain-Computer Interface (BCI) technologies enable direct communication between humans and computers by analyzing brain measurements, such as electroencephalography (EEG). BCI processing typically consists of heuristically extracting features for specific tasks, limiting the generalizability of the BCI across tasks. Here, we asked whether we can find a single generalized neural netwo...

Paralysis after stroke or neurotrauma is among the leading causes of long term disability in adults. The development of brain-computer-interface (BCI) systems that allow online classification of electric or metabolic brain activity and their translation into control signals of external devices or computers have led to two major approaches in tackling the problem of paralysis. While assistive BC...

OBJECTIVE In brain-computer interface (BCI) research, systems based on event-related potentials (ERP) are considered particularly successful and robust. This stems in part from the repeated stimulation which counteracts the low signal-to-noise ratio in electroencephalograms. Repeated stimulation leads to an optimization problem, as more repetitions also cost more time. The optimal number of rep...

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...