In recent years, there has been increased interest in using steady-state visual evoked potentials (SSVEP) in brain-computer interface (BCI) systems; the SSVEP approach currently provides the fastest and most reliable communication paradigm for the implementation of a non-invasive BCI. This paper presents recent developments in the signal processing of the SSVEP based Bremen-BCI system, which al...

Even the original developers of Bulk Current Injection now make the point that the use of BCI at aircraft level will become increasingly difficult. Direct Current Injection has the potential to replace aircraft level BCI, while also solving many of the longstanding problems of BCI. These problems include lack of synergism, inaccurate current distribution within bundles and limited numbers of in...

The application of Brain Computer Interface (BCI) technology in stroke rehabilitation represents one of the most challenging matters in the BCI field. With the aim of developing a specific BCI system for stroke rehabilitation of the upper limb we monitored the EEG sensorimotor reactivity to actual and imaged hand grasping in a group of stroke patients consecutively enrolled from a rehabilitatio...

We discuss the BCI based on inner tones and inner music. We had some success in the detection of inner tones, the imagined tones which are not sung aloud. Rather easily imagined and controlled, they offer a set of states usable for BCI, with high information capacity and high transfer rates. Imagination of sounds or musical tunes could provide a multicommand language for BCI, as if using the na...

Analyzing neural signals and providing feedback in realtime is one of the core characteristics of a brain-computer interface (BCI). As this feature may be employed to induce neural plasticity, utilizing BCI technology for therapeutic purposes is increasingly gaining popularity in the BCI community. In this paper, we discuss the state-of-the-art of research on this topic, address the principles ...

Recently, the N-way partial least squares (NPLS) approach was reported as an effective tool for neuronal signal decoding and brain-computer interface (BCI) system calibration. This method simultaneously analyzes data in several domains. It combines the projection of a data tensor to a low dimensional space with linear regression. In this paper the L1-Penalized NPLS is proposed for sparse BCI sy...

The last decade has shown a large increase in the number of P300-based BCI publications. The majority of these studies have used non-disabled subjects; far fewer studies have been conducted with people suffering from amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. Although the field has matured significantly, most research has focused on improving classification through si...

The last decade has shown a large increase in the number of P300-based BCI publications. The majority of these studies have used non-disabled subjects; far fewer studies have been conducted with people suffering from amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. Although the field has matured significantly, most research has focused on improving classification through si...

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...

Brain-Computer Interfaces (BCIs) allow users to control a computer application by brain activity as acquired, e.g., by EEG. In our classic Machine Learning approach to BCIs, the participants undertake a calibration measurement without feedback to acquire data to train the BCI system. After the training, the user can control a BCI and improve the operation through some type of feedback. However,...