Clemens Holzner

How many people are able to control a P300-based brain–computer interface (BCI)?

An EEG-based brain-computer system can be used to control external devices such as computers, wheelchairs or Virtual Environments. One of the most important applications is a spelling device to aid severely disabled individuals with communication, for example people disabled by amyotrophic lateral sclerosis (ALS). P300-based BCI systems are optimal for spelling characters with high speed and accuracy, as compared to other BCI paradigms such as motor imagery. In this study, 100 subjects tested a P300-based BCI system to spell a 5-character word with only 5 min of training. EEG data were acquired while the subject looked at a 36-character matrix to spell the word WATER. Two different versions of the P300 speller were used: (i) the row/column speller (RC) that flashes an entire column or row of characters and (ii) a single character speller (SC) that flashes each character individually. The subjects were free to decide which version to test. Nineteen subjects opted to test both versions. The BCI system classifier was trained on the data collected for the word WATER. During the real-time phase of the experiment, the subject spelled the word LUCAS, and was provided with the classifier selection accuracy after each of the five letters. Additionally, subjects filled out a questionnaire about age, sex, education, sleep duration, working duration, cigarette consumption, coffee consumption, and level of disturbance that the flashing characters produced. 72.8% (N=81) of the subjects were able to spell with 100% accuracy in the RC paradigm and 55.3% (N=38) of the subjects spelled with 100% accuracy in the SC paradigm. Less than 3% of the subjects did not spell any character correctly. People who slept less than 8h performed significantly better than other subjects. Sex, education, working duration, and cigarette and coffee consumption were not statistically related to differences in accuracy. The disturbance of the flashing characters was rated with a median score of 1 on a scale from 1 to 5 (1, not disturbing; 5, highly disturbing). This study shows that high spelling accuracy can be achieved with the P300 BCI system using approximately 5 min of training data for a large number of non-disabled subjects, and that the RC paradigm is superior to the SC paradigm. 89% of the 81 RC subjects were able to spell with accuracy 80-100%. A similar study using a motor imagery BCI with 99 subjects showed that only 19% of the subjects were able to achieve accuracy of 80-100%. These large differences in accuracy suggest that with limited amounts of training data the P300-based BCI is superior to the motor imagery BCI. Overall, these results are very encouraging and a similar study should be conducted with subjects who have ALS to determine if their accuracy levels are similar.

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.

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.

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.

Prototype of an auto-calibrating, context-aware, hybrid brain-computer interface

We present the prototype of a context-aware framework that allows users to control smart home devices and to access internet services via a Hybrid BCI system of an auto-calibrating sensorimotor rhythm (SMR) based BCI and another assistive device (Integra Mouse mouth joystick). While there is extensive literature that describes the merit of Hybrid BCIs, auto-calibrating and co-adaptive ERD BCI training paradigms, specialized BCI user interfaces, context-awareness and smart home control, there is up to now, no system that includes all these concepts in one integrated easy-to-use framework that can truly benefit individuals with severe functional disabilities by increasing independence and social inclusion. Here we integrate all these technologies in a prototype framework that does not require expert knowledge or excess time for calibration. In a first pilot-study, 3 healthy volunteers successfully operated the system using input signals from an ERD BCI and an Integra Mouse and reached average positive predictive values (PPV) of 72 and 98% respectively. Based on what we learned here we are planning to improve the system for a test with a larger number of healthy volunteers so we can soon bring the system to benefit individuals with severe functional disability.

Brain-Computer Interfaces for Virtual Environment Control

A brain-computer interface (BCI) is a new communication channel between the human brain and a digital computer. Furthermore a BCI enables communication without using any muscle activity for a subject. The ambitious goal of a BCI is finally the restoration of movements, communication and environmental control for handicapped people. However, 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 control VE for navigation and control purposes just by thoughts. A comparison of the applicability and reliability of different BCI types based on event related potentials (P300 approach) will be presented. BCI experiments for navigation in VR were conducted so far with (i) synchronous BCI and (ii) asynchronous BCI systems. A synchronous BCI analyzes the EEG patterns in a predefined time window and has 2-3 degrees of freedom. A asynchronous BCI analyzes the EEG signal continuously and if a specific event is detected then a control signal is generated. This study is focused on a BCI system that can be realized for Virtual Reality (VR) control with a high degree of freedom and high information transfer rate. Therefore a P300 based human computer interface has been developed in a VR implementation of a smart home for controlling. the environment (television, music, telephone calls) and navigation control in the house. 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. This eliminates the usage of decision trees as previously done with BCI systems.