Online Classifier Adaptation in Brain-Computer Interfaces

Brain-computer interfaces (BCIs) aim to provide a new channel of communication by enabling the subject to control an external systems by using purely mental commands. One method of doing this without invasive surgical procedures is by measuring the electrical activity of the brain on the scalp through electroencephalography (EEG). A major obstacle to developing complex EEG-based BCI systems that provide a number of intuitive mental commands is the high variability of EEG signals. EEG signals from the same subject vary considerably within a single session and between sessions on the same or different days. To deal with this we are investigating methods of adapting the classifier while it is being used by the subject. By keeping the classifier constantly tuned to the EEG signals of the current session we hope to improve the performance of the classifier and allow the subject to learn to use the BCI more effectively. This paper discusses preliminary offline and online experiments towards this goal, focusing on the initial training period when the task that the subject is trying to achieve is known and thus supervised adaptation methods can be used. In these experiments the subjects were asked to perform three mental commands (imagination of left and right hand movements, and a language task) and the EEG signals were classified with a Gaussian classifier

Towards a Robust BCI: Error Potentials and Online Learning

Recent advances in the field of Brain-Computer Interfaces (BCIs) have shown that BCIs have the potential to provide a powerful new channel of communication, completely independent of muscular and nervous systems. However, while there have been successful laboratory demonstrations, there are still issues that need to be addressed before BCIs can be used by non-experts outside the laboratory. At IDIAP we have been investigating several areas that we believe will allow us to improve the robustness, flexibility and reliability of BCIs. One area is recognition of cognitive error states, that is, identifying errors through the brain's reaction to mistakes. The production of these error potentials (ErrP) in reaction to an error made by the user is well established. We have extended this work by identifying a similar but distinct ErrP that is generated in response to an error made by the interface, (a misinterpretation of a command that the user has given). This ErrP can be satisfactorily identified in single trials and can be demonstrated to improve the theoretical performance of a BCI. A second area of research is online adaptation of the classifier. BCI signals change over time, both between sessions and within a single session, due to a number of factors. This means that a classifier trained on data from a previous session will probably not be optimal for a new session. In this paper we present preliminary results from our investigations into supervised online learning that can be applied in the initial training phase. We also discuss the future direction of this research, including the combination of these two currently separate issues to create a potentially very powerful BCI

Non-Invasive Brain Computer Interface for Mental Control of a Simulated Wheelchair

This poster presents results obtained from experiments of driving a brain-actuated simulated wheelchair that incorporates the shared-control intelligence method. The simulated wheelchair is controlled offline using band power features. The task is to drive the wheelchair along a corridor avoiding two obstacles. We have analyzed data from 4 na�ve subjects during 25 sessions carried out in two days. To measure the performance of the brain-actuated wheelchair we have compared the final position of the wheelchair with the end point of the desired trajectory. The experiments show that the incorporation of a higher intelligence level in the control device significantly helps the subject to drive the robot device

Prospects of brain–machine interfaces for space system control

The dream of controlling and guiding computer-based systems using human brain signals has slowly but steadily become a reality. The available technology allows real-time implementation of systems that measure neuronal activity, convert their signals, and translate their output for the purpose of controlling mechanical and electronic systems. This paper describes the state of the art of non-invasive brain-machine interfaces (BMIs) and critically investigates both the current technological limits and the future potential that BMIs have for space applications. We present an assessment of the advantages that BMIs can provide and justify the preferred candidate concepts for space applications together with a vision of future directions for their implementation. © 2008 Elsevier Ltd. All rights reserved

Prospects on Brain-Machine Interfaces for Space System Control

The dream of controlling and guiding computer-based systems using human brain signals has slowly but steadily become a reality. The available technology allows real-time implementation of systems that measure neuronal activity, convert their signals, and translate their output for the purpose of controlling mechanical systems. This paper describes the state of the art of non-invasive BMIs and critically investigates both the current technological limits and the future potential that BMIs have for space applications. We present an assessment of the advantages that BMIs can provide and justify the preferred candidate concepts for space applications together with a vision of future directions for their implementation

The IDIAP Brain-Computer Interface: An Asynchronous Multi-Class Approach

In this paper we give an overview of our work on a self-pace asynchronous BCI that responds every 0.5 seconds. A statistical Gaussian classifier tries to recognize three different mental tasks; it may also respond unknown for uncertain samples as the classifier has incorporated statistical rejection criteria. We report our experience with different subjects. We also describe three brain-actuated applications we have developed: a virtual keyboard, a brain game, and a mobile robot (emulating a motorized wheelchair). Finally, we discuss current research directions we are pursuing in order to improve the performance and robustness of our BCI system, especially for real-time control of brain-actuated robots

ONLINE CLASSIFIER ADAPTATION IN HIGH FREQUENCY EEG

SUMMARY: In recent years a number of noninvasive Brain-Computer Interfaces have been developed that determine the intent of a subject by analysing the Electroencephalograph(EEG) signals up to frequencies of 40Hz. The use of high frequency EEG features have recently been proposed as alternative or additional features in EEG-based BCIs. In this paper we examine the performance of several feature bands, and evaluate the performance on online classifier adaptation on these features. Our analysis shows that the higher frequency band perform very well under online classifier adaptation for all the frequency bands, particularly for the higher bands