Christoph Guger

Current trends in Graz brain-computer interface (BCI) research

This paper describes a research approach to develop a brain-computer interface (BCI) based on recognition of subject-specific EEG patterns. EEG signals recorded from sensorimotor areas during mental imagination of specific movements are classified on-line and used e.g. for cursor control. In a number of on-line experiments, various methods for EEG feature extraction and classification have been evaluated.

How many people are able to operate an EEG-based brain-computer interface (BCI)?

Ninety-nine healthy people participated in a brain-computer interface (BCI) field study conducted at an exposition held in Graz, Austria. Each subject spent 20-30 min on a two-session BCI investigation. The first session consisted of 40 trials conducted without feedback. Then, a subject-specific classifier was set up to provide the subject with feedback, and the second session - 40 trials in which the subject had to control a horizontal bar on a computer screen - was conducted. Subjects were instructed to imagine a right-hand movement or a foot movement after a cue stimulus depending on the direction of an arrow. Bipolar electrodes were mounted over the right-hand representation area and over the foot representation area. Classification results achieved with 1) an adaptive autoregressive model (39 subjects) and 2) band power estimation (60 subjects) are presented. Roughly 93% of the subjects were able to achieve classification accuracy above 60% after two sessions of training.

A Virtual Reprise of the Stanley Milgram Obedience Experiments

Background Stanley Milgrams 1960s experimental findings that people would administer apparently lethal electric shocks to a stranger at the behest of an authority figure remain critical for understanding obedience. Yet, due to the ethical controversy that his experiments ignited, it is nowadays impossible to carry out direct experimental studies in this area. In the study reported in this paper, we have used a similar paradigm to the one used by Milgram within an immersive virtual environment. Our objective has not been the study of obedience in itself, but of the extent to which participants would respond to such an extreme social situation as if it were real in spite of their knowledge that no real events were taking place. Methodology Following the style of the original experiments, the participants were invited to administer a series of word association memory tests to the (female) virtual human representing the stranger. When she gave an incorrect answer, the participants were instructed to administer an ‘electric shock’ to her, increasing the voltage each time. She responded with increasing discomfort and protests, eventually demanding termination of the experiment. Of the 34 participants, 23 saw and heard the virtual human, and 11 communicated with her only through a text interface. Conclusions Our results show that in spite of the fact that all participants knew for sure that neither the stranger nor the shocks were real, the participants who saw and heard her tended to respond to the situation at the subjective, behavioural and physiological levels as if it were real. This result reopens the door to direct empirical studies of obedience and related extreme social situations, an area of research that is otherwise not open to experimental study for ethical reasons, through the employment of virtual environments.

Rapid prototyping of an EEG-based brain-computer interface (BCI)

The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional DSP board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive model and linear discriminant analysis.

Walking from thought

Online analysis and classification of single electroencephalogram (EEG) trials during motor imagery were used for navigation in the virtual environment (VE). The EEG was recorded bipolarly with electrode placement over the hand and foot representation areas. The aim of the study was to demonstrate for the first time that it is possible to move through a virtual street without muscular activity when the participant only imagines feet movements. This is achieved by exploiting a brain-computer interface (BCI) which transforms thought-modulated EEG signals into an output signal that controls events within the VE. The experiments were carried out in an immersive projection environment, commonly referred to as a Cave (Cruz-Neira, C., Sandin, D.J., DeFanti, T.A., Surround-screen projection-based virtual reality: the design and implementation of the CAVE. Proceedings of the 20th annual conference on Computer graphics and interactive techniques, ACM Press, 1993, pp. 135-142) where participants were able to move through a virtual street by foot imagery only. Prior to the final experiments in the Cave, the participants underwent an extensive BCI training.

Current trends in hardware and software for brain–computer interfaces (BCIs)

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 different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the development of certification, dissemination and reimbursement procedures.

Walking by thinking: the brainwaves are crucial, not the muscles!

Healthy participants are able to move forward within a virtual environment (VE) by the imagination of foot movement. This is achieved by using a brain-computer interface (BCI) that transforms thought-modulated electroencephalogram (EEG) recordings into a control signal. A BCI establishes a communication channel between the human brain and the computer. The basic principle of the Graz-BCI is the detection and classification of motor-imagery-related EEG patterns, whereby the dynamics of sensorimotor rhythms are analyzed. A BCI is a closed-loop system and information is visually fed back to the user about the success or failure of an intended movement imagination. Feedback can be realized in different ways, from a simple moving bar graph to navigation in VEs. The goals of this work are twofold: first, to show the influence of different feedback types on the same task, and second, to demonstrate that it is possible to move through a VE (e.g., a virtual street) without any muscular activity, using only the imagination of foot movement. In the presented work, data from BCI feedback displayed on a conventional monitor are compared with data from BCI feedback in VE experiments with a head-mounted display (HMD) and in a high immersive projection environment (Cave). Results of three participants are reported to demonstrate the proof-of-concept. The data indicate that the type of feedback has an influence on the task performance, but not on the BCI classification accuracy. The participants achieved their best performances viewing feedback in the Cave. Furthermore the VE feedback provided motivation for the subjects.

Asynchronous P300-Based Brain-Computer Interface to Control a Virtual Environment: Initial Tests on End Users

Motor disability and/or ageing can prevent individuals from fully enjoying home facilities, thus worsening their quality of life. Advance s in the field of accessible user interfaces for domotic appliances can represent a valuable way to improve the independence of these persons. An asynchronous P300-based Brain-Computer Interface (BCI) system was recently validated with the participation of healthy young volunteers for environmental control. In this study, the asynchronous P300-based BCI for the interaction with a virtual home environment was tested with the participation of potential end-users (clients of a Frisian home care organization) with limited autonomy due to ageing and/or motor disabilities. System testing revealed that the minimum number of stimulation sequences needed to achieve correct classification had a higher intra-subject variability in potential end-users with respect to what was previously observed in young controls. Here we show that the asynchronous modality performed significantly better as compared to the synchronous mode in continuously adapting its speed to the users state. Furthermore, the asynchronous system modality confirmed its reliability in avoiding misclassifications and false positives, as previously shown in young healthy subjects. The asynchronous modality may contribute to filling the usability gap between BCI systems and traditional input devices, representing an important step towards their use in the activities of daily living.

Exploring virtual environments with an EEG-based BCI through motor imagery.

Abstract In this paper, we describe the possibility of navigating in a virtual environment using the output signal of an EEG-based Brain-Computer Interface (BCI). The graphical capabilities of virtual reality (VR) should help to create new BCI-paradigms and improve feedback presentation. The objective of this combination is to enhance the subjects learning process of gaining control of the BCI. In this study, the participant had to imagine left or right hand movements while exploring a virtual conference room. By imaging a left hand movement the subject turned virtually to the left inside the room and with right hand imagery to the right. In fact, three trained subjects reached 80% to 100% BCI classification accuracy in the course of the experimental sessions. All subjects were able to achieve a rotation in the VR to the left or right by approximately 45° during one trial. Zusammenfassung In dieser Arbeit wird die Möglichkeit der Navigation innerhalb einer virtuellen Welt unter Verwendung eines EEG-basierten Brain-Computer Interface (BCI) präsentiert. Die graphischen Fähigkeiten von Virtuellen Realitäten (VR) sollen dabei helfen neue BCI-Paradigmen zu entwicklen und die Art der Feedbackpräsentation zu verbessern. Das Ziel dieser Kombination ist es, den Lernprozess der Versuchsperson zu fördern, um eine bessere BCI-Kontrolle zu erreichen. In dieser Arbeit hatten die Probanden sich entweder eine linke oder rechte Handbewegung vorzustellen, während ein virtueller Konferenzraum erkundet wurde. Durch die Vorstelleung einer linken Handbewegung wurde die Versuchsperson virtuell im Raum nach links und durch die Vorstellung einer rechten Handbewegung nach rechts gedreht. Drei trainierte Probanden erreichten eine BCI–Klassifikationsgenauigkeit zwischen 80% und 100% im Verlauf der Messungen. Alle Probanden waren in der Lage sich virtuell nach links und rechts zu drehen und erreichten eine durchschnittliche Drehung von 45° pro Versuch.

Sharing and analyzing data from presence experiments

Presence research relies heavily on empirical experiments involving subjects in mediated environments. Since presence is a complex, multidimensional concept, experiments on presence can be extremely resource intensive and produce large amounts of data of different types. As the presence community matures, we would like to suggest that data collected in experiments be made publicly available to the community. This will allow the verification of experimental results, comparison of results of experiments carried out in different laboratories, and evaluation of new data-analysis methods. This will, eventually, lead to consistency in approaches and increased confidence in results. In this paper we present the complete dataset from a large-scale experiment that we have carried out in highly immersive virtual reality. We describe the data we have gathered and give examples of the types of analysis that can be made based on that data.