Bernhard Graimann

Spatiotemporal patterns of beta desynchronization and gamma synchronization in corticographic data during self-paced movement

OBJECTIVE To study the spatiotemporal pattern of event-related desynchronization (ERD) and event-related synchronization (ERS) in electrocorticographic (ECoG) data with closely spaced electrodes. METHODS Four patients with epilepsy performed self-paced hand movements. The ERD/ERS was quantified and displayed in the form of time-frequency maps. RESULTS In all subjects, a significant beta ERD with embedded gamma ERS was found. CONCLUSIONS Self-paced movement is accompanied not only by a relatively widespread mu and beta ERD, but also by a more focused gamma ERS in the 60-90 Hz frequency band.

An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate

An improvement of the information transfer rate of brain-computer communication is necessary for the creation of more powerful and convenient applications. This paper presents an asynchronously controlled three-class brain-computer interface-based spelling device [virtual keyboard (VK)], operated by spontaneous electroencephalogram and modulated by motor imagery. Of the first results of three able-bodied subjects operating the VK, two were successful, showing an improvement of the spelling rate /spl sigma/, the number of correctly spelled letters/min, up to /spl sigma/=3.38 (average /spl sigma/=1.99).

Graz-BCI: state of the art and clinical applications

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.

Continuous EEG classification during motor imagery-simulation of an asynchronous BCI

Nearly all electroencephalogram (EEG)-based brain-computer interface (BCI) systems operate in a cue-paced or synchronous mode. This means that the onset of mental activity (thought) is externally-paced and the EEG has to be analyzed in predefined time windows. In the near future, BCI systems that allow the user to intend a specific mental pattern whenever she/he wishes to produce such patterns will also become important. An asynchronous BCI is characterized by continuous analyzing and classification of EEG data. Therefore, it is important to maximize the hits (true positive rate) during an intended mental task and to minimize the false positive detections in the resting or idling state. EEG data recorded during right/left motor imagery is used to simulate an asynchronous BCI. To optimize the classification results, a refractory period and a dwell time are introduced.

The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges

Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e, the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time (<;200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.

Seperability of four-class motor imagery data using independent components analysis

This paper compares different ICA preprocessing algorithms on cross-validated training data as well as on unseen test data. The EEG data were recorded from 22 electrodes placed over the whole scalp during motor imagery tasks consisting of four different classes, namely the imagination of right hand, left hand, foot and tongue movements. Two sessions on different days were recorded for eight subjects. Three different independent components analysis (ICA) algorithms (Infomax, FastICA and SOBI) were studied and compared to common spatial patterns (CSP), Laplacian derivations and standard bipolar derivations, which are other well-known preprocessing methods. Among the ICA algorithms, the best performance was achieved by Infomax when using all 22 components as well as for the selected 6 components. However, the performance of Laplacian derivations was comparable with Infomax for both cross-validated and unseen data. The overall best four-class classification accuracies (between 33% and 84%) were obtained with CSP. For the cross-validated training data, CSP performed slightly better than Infomax, whereas for unseen test data, CSP yielded significantly better classification results than Infomax in one of the sessions.

15 years of BCI research at graz university of technology: current projects

Over the last 15 years, the Graz Brain-Computer Interface (BCI) has been developed and all components such as feature extraction and classification, mode of operation, mental strategy, and type of feedback have been investigated. Recent projects deal with the development of asynchronous BCIs, the presentation of feedback and applications for communication and control.

Event-related beta EEG-changes during passive and attempted foot movements in paraplegic patients

A number of electroencephalographic (EEG) studies report on motor event-related desynchronization and synchronization (ERD/ERS) in the beta band, i.e. a decrease and increase of spectral amplitudes of central beta rhythms in the range from 13 to 35 Hz. Following an ERD that occurs shortly before and during the movement, bursts of beta oscillations (beta ERS) appear within a 1-s interval after movement offset. Such a post-movement beta ERS has been reported after voluntary hand movements, passive movements, movement imagination, and also after movements induced by functional electrical stimulation. The present study compares ERD/ERS patterns in paraplegic patients (suffering from a complete spinal cord injury) and healthy subjects during attempted (active) and passive foot movements. The aim of this work is to address the question, whether patients do have the same focal beta ERD/ERS pattern during attempted foot movement as healthy subjects do. The results showed midcentral-focused beta ERD/ERS patterns during passive, active, and imagined foot movements in healthy subjects. This is in contrast to a diffuse and broad distributed ERD/ERS pattern during attempted foot movements in patients. Only one patient showed a similar ERD/ERS pattern. Furthermore, no significant ERD/ERS patterns during passive foot movement in the group of the paraplegics could be found.

Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis

Highly accurate asynchronous detection of movement related patterns in individual electrocorticogram channels has been shown using detection based on either event-related potentials (ERPs) or event-related desynchronization and synchronization (ERD/ERS). A method using wavelet-packet features selected with a genetic algorithm was proposed to simultaneously detect ERP and ERD/ERS and was tested on data from seven subjects and four motor tasks. The proposed wavelet method performed better than previous methods with perfect detection for four subject/task combinations and hit percentages greater than 90% with false positive percentages less than 15% for at least one task for all seven subjects.

Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis

Three independent components analysis (ICA) algorithms (Infomax, FastICA and SOBI) have been compared with other preprocessing methods in order to find out whether and to which extent spatial filtering of EEG data can improve single trial classification accuracy. As reference methods, common spatial patterns (CSP) (a supervised method, whereas all ICA algorithms are unsupervised), bipolar derivations and the original raw monopolar data were used. In addition to only performing ICA, the number of components was reduced with PCA before calculating a spatial filter for Infomax and FastICA. The multichannel data (22 channels) of eight subjects, consisting of two sessions recorded on different days, was analyzed. The task was to perform motor imagery of the left hand, right hand, foot or tongue, respectively, during predefined time slices (cued paradigm). For a measure of fitness, classification accuracies for both cross-validated results using data from just one session as well as simulated online results (representing the session-to-session transfer) were calculated. In the latter case, the spatial filters and classifiers were computed for one session and applied to the completely unseen second session. For the data analyzed in this study, Infomax outperformed the other two ICA variants by far, both in the cross-validated as well as in the simulated online case. CSP, on the other hand, yielded significantly lower classification accuracies than Infomax for the cross-validated results, whereas there is no statistically significant difference when it comes to simulated online data. Performing PCA before ICA improved the results in the case of FastICA, whereas the classification accuracies dropped significantly for Infomax.