Bernhard Obermaier’s research while affiliated with Graz University of Technology and other places

A "virtual keyboard" (VK) is a letter spelling device operated for example by spontaneous electroencephalogram (EEG), whereby the EEC is modulated by mental hand and leg motor imagery. We report on three able-bodied subjects, operating the VK. The ability in the use of the VK varies between 0.85 and 0.5 letters/min in error-free writing.

The Graz-brain-computer interface (BCI) is a cue-based system using the imagery of motor action as the appropriate mental task. Relevant clinical applications of BCI-based systems for control of a virtual keyboard device and operations of a hand orthosis are reported. Additionally, it is demonstrated how information transfer rates of 17 b/min can be acquired by real time classification of oscillatory activity.

EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable variations in the signal due to artefacts or recogniser-subject feedback. A number of techniques were recently developed to address a related problem of recogniser robustness to uncontrollable signal variation which also occurs in automatic speech recognition (ASR). In this article we consider how some of the proved advantages of the "multi-stream combination" and "tandem" approaches in HMM/ANN hybrid based ASR can possibly be applied to improve the performance of EEG recognition.

EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable variations in the signal due to artefacts or recogniser-subject feedback. A number of techniques were recently developed to address a related problem of recogniser robustness to uncontrollable signal variation which also occurs in automatic speech recognition (ASR). In this article we consider how some of the proved advantages of the "multi-stream combination" and "tandem" approaches in HMM/ANN hybrid based ASR can possibly be applied to improve the performance of EEG recognition. Keywords: EEG, multi-stream classification, robust recognition Acknowledgements: This work was supported by the EC/OFES (European Community / Swiss Federal Office for Education and Science) RESPITE project (REcognition of Speech by Partial Information TEchniques). Contents 1.

Classification of the electroencephalogram (EEG) during motor imagery of the left or right hand can be performed using a classifier comprising two hidden Markov models (HMMs) describing the spatio-temporal patterns related to the imagination. Due to the known asymmetries during motor imagery of rightand left-hand movement, an HMM-based classifier allowing asymmetrical structures is introduced. The comparison between such a system and a symmetrical one is based on the error rate of classification. The results for EEG data collected during 20 sessions from five subjects demonstrate a significant improvement of 9% for the classification accuracy for the asymmetric classifiers. The selection of the DAM for classification is done using a variant of genetic algorithms (GAs); namely, the adaptive reservoir genetic algorithm (ARGA)

Hidden Markov models (HMMs) are presented for the online classification of single trial EEG data during imagination of a left or right hand movement. The classification shows an improvement of the online experiment and the temporal determination of minimal classification error compared to linear classification methods.

The information transfer rate, given in bits per trial, is used as an evaluation measurement in a brain-computer interface (BCI). Three subjects performed four motor-imagery (left hand, right hand, foot, and tongue) and one mental-calculation task. Classification of the electroencephalogram (EEG) patterns is based on band power estimates and hidden Markov models (HMMs). We propose a method that combines the EEG patterns based on separability into subsets of two, three, four, and five mental tasks. The information transfer rates of the BCI systems comprised of these subsets are reported. The achieved information transfer rates vary from 0.42 to 0.81 bits per trial and reveal that the upper limit of different mental tasks for a BCI system is three. In each subject, different combinations of three tasks resulted in the best performance.

A 'Virtual Keyboard' (VK) is a letter spelling device operated by spontaneous EEG, whereby the EEG is modulated by mental hand and leg motor imagery. We report on a tetraplegic patient initially trained on an EEG-based orthosis control system operating the VK. He achieved an outstanding ability in the use of the VK and can operate it with 0.95 letters per minute.

The Graz Brain-Computer Interface (BCI) analyses and classifies the dynamics of oscillatory EEG components during motor imagery. At this time a patient controls the closing and opening of a hand orthosis with a BCI and students are able to write error free with a spelling rate of 1 character/minute.

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.