Günter Edlinger

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.

Virtual Smart Home Controlled by Thoughts

An electroencephalogram (EEG) based brain-computer interface (BCI) was connected to a virtual reality (VR) system in order to control a smart home application. Therefore special control masks were developed which allowed using the P300component of the EEG as input signal for the BCI system. Control commands for switching TV channels, for opening and closing doors and windows,for navigation and conversation were realized.Experiments with 12 subjects were made to investigate the speed and accuracy that can be achieved if several hundred of commands are used to control the smart home environment.The study clearly shows that such a BCI system can be used for smart home control. The Virtual Reality approach is a very cost effective way for testing the smart home environment together with the BCI system.

A hybrid brain-computer interface for smart home control

Brain-computer interfaces (BCI) provide a new communication channel between the human brain and a computer without using any muscle activities. Applications of BCI systems comprise communication, restoration of movements or environmental control. Within this study we propose a combined P300 and steady-state visually evoked potential (SSVEP) based BCI system for controlling finally a smart home environment. Firstly a P300 based BCI system was developed and tested in a virtual smart home environment implementation to work with a high accuracy and a high degree of freedom. Secondly, in order to initiate and stop the operation of the P300 BCI a SSVEP based toggle switch was implemented. Results indicate that a P300 based system is very well suitable for applications with several controllable devices and where a discrete control command is desired. A SSVEP based system is more suitable if a continuous control signal is needed and the number of commands is rather limited. The combination of a SSVEP based BCI as a toggle switch to initiate and stop the P300 selection yielded in all subjects very high reliability and accuracy.

SSVEP based brain-computer interface for robot control

A brain computer interface (BCI) using steady state visual evoked potentials (SSVEP) is presented. EEG was derived from 3 subjects to test the suitability of SSVEPs for robot control. To calculate features and to classify the EEG data Minimum Energy and Fast Fourier Transformation (FFT) with linear discriminant analysis (LDA) were used. Finally the change rate (fluctuation of the classification result) and the majority weight of the analysis algorithms were calculated to increase the robustness and to provide a zero-class classification. The implementation was tested with a robot that was able to move forward, backward, to the left and to the right and to stop. A high accuracy was achieved for all commands. Of special interest is that the robot stopped with high reliability if the subject did not watch at the stimulation LEDs and therefore successfully zero-class recognition was implemented.

Goal-Oriented Control with Brain-Computer Interface

A brain-computer interface (BCI) is a new communication channel between the human brain and a digital computer. Such systems have been designed to support disabled people for communication and environmental control. In more recent research also BCI control in combination with Virtual Environments (VE) gains more and more interest. Within this study we present experiments combining BCI systems and VE for navigation and control purposes just by thoughts. Results show that the new P300 based BCI system allows a very reliable control of the VR system. Of special importance is the possibility to select very rapidly the specific command out of many different choices. The study suggests that more than 80% of the population could use such a BCI within 5 minutes of training only. This eliminates the usage of decision trees as previously done with BCI systems.

Real-time position reconstruction with hippocampal place cells.

Brain–computer interfaces (BCI) are using the electroencephalogram, the electrocorticogram and trains of action potentials as inputs to analyze brain activity for communication purposes and/or the control of external devices. Thus far it is not known whether a BCI system can be developed that utilizes the states of brain structures that are situated well below the cortical surface, such as the hippocampus. In order to address this question we used the activity of hippocampal place cells (PCs) to predict the position of an rodent in real-time. First, spike activity was recorded from the hippocampus during foraging and analyzed off-line to optimize the spike sorting and position reconstruction algorithm of rats. Then the spike activity was recorded and analyzed in real-time. The rat was running in a box of 80 cm × 80 cm and its locomotor movement was captured with a video tracking system. Data were acquired to calculate the rats trajectories and to identify place fields. Then a Bayesian classifier was trained to predict the position of the rat given its neural activity. This information was used in subsequent trials to predict the rats position in real-time. The real-time experiments were successfully performed and yielded an error between 12.2 and 17.4% using 5–6 neurons. It must be noted here that the encoding step was done with data recorded before the real-time experiment and comparable accuracies between off-line (mean error of 15.9% for three rats) and real-time experiments (mean error of 14.7%) were achieved. The experiment shows proof of principle that position reconstruction can be done in real-time, that PCs were stable and spike sorting was robust enough to generalize from the training run to the real-time reconstruction phase of the experiment. Real-time reconstruction may be used for a variety of purposes, including creating behavioral–neuronal feedback loops or for implementing neuroprosthetic control.

Post-movement beta oscillations studied with linear estimation.

The application of surface laplacian and linear estimation methods to single trial EEG data was studied. EEG was recorded in 3 subjects during voluntary, self-paced extensions and flexions of the index finger. In each subject a post-movement beta synchronisation was found in specific frequency bands. The surface laplacian estimates were calculated using spherical splines and cortical current distributions were constructed using the linear estimation method. Both methods yield similar results and reveal a maximal event-related synchronisation over the left sensorimotor area approximately 500-750 ms after termination of movement.

Architectures of laboratory-PC and mobile pocket PC brain-computer interfaces

An EEG-based brain computer interface (BCI) system converts brain activity into control signals. A BCI system has to satisfy different demands depending on the application area. A laboratory PC based system allows the flexible design of multiple/single channel feature extraction, classification methods and experimental paradigms. The key advantage of a pocket PC BCI approach is its small dimension and battery supply. Hence a mobile BCI system e.g. mounted on a wheelchair can be realized

Effects of a fast cable car ascent to an altitude of 2700 meters on EEG and ECG.

In the Eastern Alps, the Dachstein massif with a height of almost 3000 m is an ideal location for investigating the effects of changes in altitude on the human body. Within a few minutes, a cable car facilitates an ascent from 1702 to 2700 m above sea level, where the partial pressure of oxygen is about 550 mmHg (as compared to 760 mmHg at sea level). In this study, 10 healthy subjects performed a reaction time task at 990 m and 2700 m in altitude. The subjects were instructed to perform a right hand index finger movement as fast as possible after a green light flashed (repeated 50 times). The corresponding electrocardiogram (ECG) and the electroencephalogram (EEG) were recorded. From the ECG heart rate and heart rate variability measures in the time and frequency domain were calculated. An event-related desynchronization/synchronization (ERD/ERS) analysis was performed with the EEG data. Finally, the EEG activity and the ECG parameters were correlated. The study showed that with the fast ascent to 2700 m the heart rate increased and the heart rate variability measures decreased. The correlation analysis indicated a close relationship between the EEG activity and the heart rate and heart rate variability. Furthermore it was shown for the first time that the beta ERS in the 14-18 Hz frequency range (post-movement beta ERS) was significantly reduced at high altitude. Very interesting also is the loss of correlation between EEG activity and cardiovascular measures during finger movement at high altitude. The suppressed post-movement beta ERS at the altitude of 2700 m may be interpreted as results of an increased cortical excitability level when compared with the reference altitude at 990 m above sea level.

Using a P300 Brain Computer Interface for Smart Home Control

An electroencephalogram (EEG) based brain-computer interface (BCI) was connected with a Virtual Reality system in order to control a smart home application. Therefore special control masks were developed which allowed using the P300 component of the EEG as input signal for the BCI system. Control commands for switching TV channels, for opening and closing doors and windows, for navigation and conversation were realized. Experiments with 12 subjects were made to investigate the speed and accuracy that can be achieved if several hundred of commands are used to control the smart home environment.