Operating brain-actuated devices requires split attention between the interaction of the device with its environment and the Brain-Computer Interface (BCI) feedback. In case of screen-based applications it is possible to merge BCI feedback and application, but not in case of controlling devices, like wheelchairs or exoskeletons. Recently we demonstrated that BCI feedback could be provided via t...

The brain computer interface (BCI) should be the accessibility solution “par excellence” for interactive and e-learning systems. There is a substantial tradition of research on the human electro encephalogram (EEG) and on BCI systems that are based, inter alia, on EEG measurement. We have not yet seen a viable BCI for e-learning. For many users for a BCI based interface is their first choice fo...

We have integrated the Graz brain-computer interface (BCI) system with a highly-immersive virtual reality (VR) Cave-like system. This setting allows for a new type of experience, whereby participants can control a virtual world using imagination of movement. However, current BCI systems still have many limitations. In this paper we present two experiments exploring the different constraints pos...

A brain-computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize...

Changes in psychological state have been proposed as a cause of variation in brain-computer interface performance, but little formal analysis has been conducted to support this hypothesis. In this study, we investigated the effects of three mental states-fatigue, frustration, and attention-on BCI performance. Twelve able-bodied participants were trained to use a two-class EEG-BCI based on the p...

A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and ef...

This paper surveys existing and past research on brain-computer interfaces (BCI) for implicit human-computer interaction. A novel way of using BCI has indeed emerged, which proposes to use BCI in a less explicit way : the so-called “passive” BCI. Implicit BCI or passive BCI refers to BCI in which the user does not try to control his brain activity. Thus the brain activity is assimilated to an i...

To date, the application of brain computer interface (BCI) technology in rehabilitation represents one of the most challenging matters in the BCI filed. With the aim of developing a specific BCI system for speech language rehabilitation, this paper describes framework of a novel BCI system designed for clinical settings in speech language rehabilitation. It presents an overview of the technolog...

In this dissertation we present the results of our effort to provide a modular, completely freeand open source brain-computer interface (BCI) system in Python. While other BCI systems exist, they are usually monolithic and written in a low level programming language like C++. Monolithic BCI systems cover the whole BCI processing chain from data acquisition, to signal processing, and feedbackand...

We present a feasibility study of a novel hybrid brain-computer interface (BCI) system for advanced functional electrical therapy (FET) of grasp. FET procedure is improved with both automated stimulation pattern selection and stimulation triggering. The proposed hybrid BCI comprises the two BCI control signals: steady-state visual evoked potentials (SSVEP) and event-related desynchronization (E...