Chapter 9 Flexibility and Practicality

“Graz brain–computer interface (BCI)” transforms changes in oscillatory electroencephalogram (EEG) activity into control signals for external devices and feedback. Steady‐state evoked potentials (SSEPs) and event‐related desynchronization (ERD) are employed to encode user messages. User‐specific setup and training are important issues for robust and reliable classification. Furthermore, in order to implement small and thus affordable systems, focus is put on the minimization of the number of EEG sensors. The system also supports the self‐paced operation mode, that is, users have on‐demand access to the system at any time and can autonomously initiate communication. Flexibility, usability, and practicality are essential to increase user acceptance. Here, we illustrate the possibilities offered by now from EEG‐based communication. Results of several studies with able‐bodied and disabled individuals performed inside the laboratory and in real‐world environments are presented; their characteristics are shown and open issues are mentioned. The applications include the control of neuroprostheses and spelling devices, the interaction with Virtual Reality, and the operation of off‐the‐shelf software such as Google Earth.

Introduction

A brain–computer interface (BCI) is a communication system that allows the user to bypass the efferent pathways of the central nervous system and thus to directly link the human brain with the machine. The motivation for the development of this nonmuscular communication channel is to replace, or at least to somewhat enhance, the lost motor functions of physically disabled persons with intact cortical signals. These include individuals suffering from strokes, spinal cord injuries, or with degenerative diseases like amyotrophic lateral sclerosis. Able‐bodied individuals may find BCI‐based communication inaccurate and slow compared to their intact motor control abilities. The disabled, however, learned to deal successfully with this technology in their familiar surroundings and to operate spelling devices (Neuper et al., 2006) or neuroprostheses (Müller‐Putz et al., 2005a, Pfurtscheller et al., 2003). Under circumstances or in environments where the body behaves differently than under usual conditions, for example, as in space, such an additional “hands‐free” communication channel, however, can be advantageous also for able‐bodied users.

Here, we give an overview of Graz‐BCI research and illustrate, by means of different practical applications, the possibilities this kind of technology offers at the present time. One major aim of our research is to enhance usability, practicality, and flexibility of BCI‐based interaction. Important issues in this context are the simplification of the hardware and sensor technology, the reduction of the user training period, and the increased robustness and reliability of the signal processing methods employed.