Christoph Hintermüller

How Many People Could Use an SSVEP BCI

Brain-computer interfaces (BCI) are communication systems that allow people to send messages or commands without movement. BCIs rely on different types of signals in the electroencephalogram (EEG), typically P300s, steady-state visually evoked potentials (SSVEP), or event-related desynchronization. Early BCI systems were often evaluated with a selected group of subjects. Also, many articles do not mention data from subjects who performed poorly. These and other factors have made it difficult to estimate how many people could use different BCIs. The present study explored how many subjects could use an SSVEP BCI. We recorded data from 53 subjects while they participated in 1–4 runs that were each 4 min long. During these runs, the subjects focused on one of four LEDs that each flickered at a different frequency. The eight channel EEG data were analyzed with a minimum energy parameter estimation algorithm and classified with linear discriminant analysis into one of the four classes. Online results showed that SSVEP BCIs could provide effective communication for all 53 subjects, resulting in a grand average accuracy of 95.5%. About 96.2% of the subjects reached an accuracy above 80%, and nobody was below 60%. This study showed that SSVEP based BCI systems can reach very high accuracies after only a very short training period. The SSVEP approach worked for all participating subjects, who attained accuracy well above chance level. This is important because it shows that SSVEP BCIs could provide communication for some users when other approaches might not work for them.

Cardiac anisotropy: is it negligible regarding noninvasive activation time imaging?

The aim of this study was to quantify the effect of cardiac anisotropy in the activation-based inverse problem of electrocardiography. Differences of the patterns of simulated body surface potential maps for isotropic and anisotropic conditions were investigated with regard to activation time (AT) imaging of ventricular depolarization. AT maps were estimated by solving the nonlinear inverse ill-posed problem employing spatio-temporal regularization. Four different reference AT maps (sinus rhythm, right-ventricular and septal pacing, accessory pathway) were calculated with a bidomain theory based anisotropic finite-element heart model in combination with a cellular automaton. In this heart model a realistic fiber architecture and conduction system was implemented. Although the anisotropy has some effects on forward solutions, effects on inverse solutions are small indicating that cardiac anisotropy might be negligible for some clinical applications (e.g., imaging of focal events) of our AT imaging approach. The main characteristic events of the AT maps were estimated despite neglected electrical anisotropy in the inverse formulation. The worst correlation coefficient of the estimated AT maps was 0.810 in case of sinus rhythm. However, all characteristic events of the activation pattern were found. The results of this study confirm our clinical validation studies of noninvasive AT imaging in which cardiac anisotropy was neglected.

A BCI using VEP for continuous control of a mobile robot

A brain-computer interface (BCI) translates brain activity into commands to control devices or software. Common approaches are based on visual evoked potentials (VEP), extracted from the electroencephalogram (EEG) during visual stimulation. High information transfer rates (ITR) can be achieved using (i) steady-state VEP (SSVEP) or (ii) code-modulated VEP (c-VEP). This study investigates how applicable such systems are for continuous control of robotic devices and which method performs best. Eleven healthy subjects steered a robot along a track using four BCI controls on a computer screen in combination with feedback video of the movement. The average time to complete the tasks was (i) 573.43 s and (ii) 222.57 s. In a second non-continuous trial-based validation run the maximum achievable online classification accuracy over all subjects was (i) 91.36 % and (ii) 98.18 %. This results show that the c-VEP fits the needs of a continuous system better than the SSVEP implementation.

Multitask Humanoid Control with a Brain-Computer Interface: user experiment with HRP-2

In this paper, we present our approach to design a brain-computer interface (BCI) that allows the user to perform multitask humanoid control. We efficiently integrate techniques from computer vision and the task-function based control together with the brain-computer interface into an immersive and intuitive control application despite the well-known shortcomings of BCI. This approach is assessed in a user experiment involving 4 subjects who successfully controlled the HRP-2 humanoid robot in a scenario involving both grasping tasks and steering. The user experiences and the interface performances are presented and give a rich insight into future research that can be made to improve and extend such interface.

Augmented control of an avatar using an SSVEP based BCI

The demonstration shows the usage of an EEG-based brain-computer interface (BCI) for the real-time control of an avatar in World of Warcraft. Visitors can test the installation during the conference after about 5 minutes of training time. World of Warcraft is a common Massively Multiplayer Online Role-Playing Game (MMORPG) in which the player controls an avatar in a virtual environment. The user has to wear newly developed dry EEG electrodes which are connected to a biosignal amplifier. Then the data is transmitted to a computer to perform the real-time analysis of the EEG data. The BCI system is using steady-state visual evoked potentials (SSVEPs) as control signal. Therefore the system shows different icons flickering with different frequencies. If the user focuses now on one of the icons the flickering frequency is visible in the EEG data and can be extracted with frequency analysis algorithms. In order to control an avatar in World of Warcraft it is necessary to have 4 control icons that are analyzed in real-time. Three icons are necessary to turn left or right or to move forward. Additionally a 4th icon is required to perform certain actions like grasping objects, attacking other objects....like shown in Figure 1. The visual stimulation took place via a 60Hz LCD-display with flickering frequencies of 15, 12, 10 and 8.57Hz in combination with an underlying video. To visualize the flickering controls a BCI-Overlay library based on OpenGL was implemented, which can be used by any graphics application. It provides the possibility to generate BCI controls within a virtual reality environment or as overlays in combination with video sequences Figure 2 shows the components of the complete system. The user is connected with 8 EEG electrodes to the BCI system that is running under Windows and MATLAB. The BCI system uses the minimum energy algorithm and a linear discriminant analysis to determine if the user is looking at one of the icons or if the user is not attending. Via a UDP communication channel the BCI system is controlling the BCI-Overlay module that generates the 4 flickering icons around the WoW User Interface. If the BCI system detects a certain command it is transmitted to the game controller which generates the corresponding WoW command. This is straight forward for the left, right and move forward commands, but more complicated for the action command. Action commands are context dependant and the controller has to select certain possible actions. Finally the command is transmitted to WoW and the avatar performs the action. This allows the user to play WoW with the BCI system only by thought.

A P300 BCI for e - inclusion, cognitive rehabilitation and smart home control

We implemented an easy-to-use P300 BCI system that allows users to control a variety of applications for communication, creative expression, training of cognitive abilities and environmental control. In this paper we present an evaluation of the following four applications: a speller, two games that can be used for cognitive rehabilitation or entertainment, twitter (via web browser) and a webcam. All fourteen healthy participants had control over the BCI and reached high accuracies (>85%). The results of the evaluation informed the development of the next prototype. With a user-centered approach we aim to further improve the prototype and ultimately provide end users with a multifunctional system that can be used as assistive technology in a home environment.

Multi-lead ECG electrode array for clinical application of electrocardiographic inverse problem

Methods for noninvasive imaging of electric function of the heart might become clinical standard procedure the next years. Thus, the overall procedure has to meet clinical requirements as easy and fast application. In this study we propose a new electrode array which improves the information content in the ECG map, considering clinical constraints such as easy to apply and compatibility with routine leads. A major challenge is the development of an electrode array which yields a high information content even for a large interindividual variation in torso shape. For identifying regions of high information content we introduce the concept of a locally applied virtual electrode array. As a result of our analysis we constructed a new electrode array consisting of two L-shaped regular spaced parts and compared it to the electrode array we use for clinical studies upon activation time imaging. We assume that one side effect caused by the regular shape and spacing of the new array be that the reconstruction of electrodes placed on the patients back is simplified. It may be sufficient to record a few characteristic electrode positions and merge them with a model of the posterior array.

A Multifunctional Brain-Computer Interface Intended for Home Use: An Evaluation with Healthy Participants and Potential End Users with Dry and Gel-Based Electrodes

Current brain-computer interface (BCIs) software is often tailored to the needs of scientists and technicians and therefore complex to allow for versatile use. To facilitate home use of BCIs a multifunctional P300 BCI with a graphical user interface intended for non-expert set-up and control was designed and implemented. The system includes applications for spelling, web access, entertainment, artistic expression and environmental control. In addition to new software, it also includes new hardware for the recording of electroencephalogram (EEG) signals. The EEG system consists of a small and wireless amplifier attached to a cap that can be equipped with gel-based or dry contact electrodes. The system was systematically evaluated with a healthy sample, and targeted end users of BCI technology, i.e., people with a varying degree of motor impairment tested the BCI in a series of individual case studies. Usability was assessed in terms of effectiveness, efficiency and satisfaction. Feedback of users was gathered with structured questionnaires. Two groups of healthy participants completed an experimental protocol with the gel-based and the dry contact electrodes (N = 10 each). The results demonstrated that all healthy participants gained control over the system and achieved satisfactory to high accuracies with both gel-based and dry electrodes (average error rates of 6 and 13%). Average satisfaction ratings were high, but certain aspects of the system such as the wearing comfort of the dry electrodes and design of the cap, and speed (in both groups) were criticized by some participants. Six potential end users tested the system during supervised sessions. The achieved accuracies varied greatly from no control to high control with accuracies comparable to that of healthy volunteers. Satisfaction ratings of the two end-users that gained control of the system were lower as compared to healthy participants. The advantages and disadvantages of the BCI and its applications are discussed and suggestions are presented for improvements to pave the way for user friendly BCIs intended to be used as assistive technology by persons with severe paralysis.

Brain Neural Computer Interface for Everyday Home Usage

In the last years, Brain Neural Computer Interfaces (BNCIs) have been investigated and several applications have been proposed. Those systems have been explored almost exclusively in laboratories with developers and researchers. Home usage has been demonstrated, though only with on-going expert supervision. In this paper, we present a BNCI for everyday home usage. The proposed system is aimed at supporting the autonomy and independence of people living at home with a disability. The overall system is currently installed in three end-users’ home in Belfast.

Brain–Computer Interfaces on Track to Home: Results of the Evaluation at Disabled End-Users’ Homes and Lessons Learnt.

The BackHome system is a multi-functional BCI system, the final outcome of a User Centred Design approach, whose ambition is to move BCI systems from laboratories into the home of people in need for their independent home use. The paper presents the results of testing and evaluation of the BackHome system with end-users at their own homes. Results show moderate to good acceptance from end-users, caregivers and therapists; which reported promising usability levels, good user satisfaction and levels of control in the use of services and home support based on remote monitoring tools.