Relieving phantom limb pain with multimodal sensory-motor training.

OBJECTIVE: The causes for the disabling condition of phantom limb pain (PLP), affecting 85% of amputees, are so far unknown, with few effective treatments available. Sensory feedback based strategies to normalize the motor commands to control the phantom limb offer important targets for new effective treatments as the correlation between phantom limb motor control and sensory feedback from the motor intention has been identified as a possible mechanism for PLP development. APPROACH: Ten upper-limb amputees, suffering from chronic PLP, underwent 16 days of intensive training on phantom-limb movement control. Visual and tactile feedback, driven by muscular activity at the stump, was provided with the aim of reducing PLP intensity. MAIN RESULTS: A 32.1% reduction of PLP intensity was obtained at the follow-up (6 weeks after the end of the training, with an initial 21.6% reduction immediately at the end of the training) reaching clinical effectiveness for chronic pain reduction. Multimodal sensory-motor training on phantom-limb movements with visual and tactile feedback is a new method for PLP reduction. SIGNIFICANCE: The study results revealed a substantial reduction in phantom limb pain intensity, obtained with a new training protocol focused on improving phantom limb motor output using visual and tactile feedback from the stump muscular activity executed to move the phantom limb.

A comparison approach toward finding the best feature and classifier in cue-based BCI.

In this paper, a comparative evaluation of state-of-the art feature extraction and classification methods is presented for five subjects in order to increase the performance of a cue-based Brain-Computer interface (BCI) system for imagery tasks (left and right hand movements). To select an informative feature with a reliable classifier features containing standard bandpower, AAR coefficients, and fractal dimension along with support vector machine (SVM), Adaboost and Fisher linear discriminant analysis (FLDA) classifiers have been assessed. In the single feature-classifier combinations, bandpower with FLDA gave the best results for three subjects, and fractal dimension and FLDA and SVM classifiers lead to the best results for two other subjects. A genetic algorithm has been used to find the best combination of the features with the aforementioned classifiers and led to dramatic reduction of the classification error and also best results in the four subjects. Genetic feature combination results have been compared with the simple feature combination to show the performance of the Genetic algorithm.

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.

Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data.

OBJECTIVES: Analysis of event-related desynchronization (ERD) and event-related synchronization (ERS) often requires the investigation of diverse frequency bands. Such analysis can be difficult, especially when using multichannel data. Therefore, an effective method for the visualization of event-related changes in oscillatory brain activity is required. METHODS: A bootstrap-based method is presented which gives time-frequency maps showing only significant changes of ERD or ERS in predetermined frequency bands. RESULTS: Examples from an electroencephalographic study and an electrocorticographic study are shown. The results demonstrate how easily reactive channels and their spatio-temporal and frequency-specific characteristics can be identified by means of this method. CONCLUSIONS: The proposed method is a simple but effective way to visualize significant ERD/ERS patterns.

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.

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.

Brain-computer communication based on the dynamics of brain oscillations.

This chapter presents a review of brain-computer communication based on motor imagery and the dynamics of brain oscillations. The concept of motor imagery as experimental strategy and the two different modes of operation a brain-computer interface can have are explained. An EEG based brain switch that can control a FES-induced hand grasp of a tetraplegic and an approach towards an ECoG based brain switch are presented.

Frequency component selection for an ECoG-based brain-computer interface.

The aim of the present study was to investigate the most significant frequency components in electrocorticogram (ECoG) recordings in order to operate a brain computer interface (BCI). For this purpose the time-frequency ERD/ERS map and the distinction sensitive learning vector quantization (DSLVQ) are applied to ECoG from three subjects, recorded during a self-paced finger movement. The results show that the ERD/ERS pattern found in ECoG generally matches the ERD/ERS pattern found in EEG recordings, but has an increased prevalence of frequency components in the beta range.