Showing papers by "Gert Pfurtscheller published in 1998"

Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters

Abstract: Electroencephalogram (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by, e.g., patients with amyotrophic lateral sclerosis (ALS) to develop a simple binary response in order to reply to specific questions. Four subjects participated in a series of on-line sessions with an EEG-based cursor control. The EEG was recorded from electrodes overlying sensory-motor areas during left and right motor imagery. The EEG signals were analyzed in subject-specific frequency bands and classified on-line by a neural network. The network output was used as a feedback signal. The on-line error (100%-perfect classification) was between 10.0 and 38.1%. In addition, the single-trial data were also analyzed off-line by using an adaptive autoregressive (AAR) model of order 6. With a linear discriminant analysis the estimated parameters for left and right motor imagery were separated. The error rate, obtained varied between 5.8 and 32.8% and was, on average, better than the on-line results. By using the AAR-model for on-line classification an improvement in the error rate can be expected, however, with a classification delay around 1 s.

EEG-based communication: improved accuracy by response verification

Abstract: Humans can learn to control the amplitude of electroencephalographic (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could provide a new augmentative communication channel for individuals with motor disabilities. In the present system, each dimension of cursor movement is controlled by a linear equation. While the intercept in the equation is continually updated, it does not perfectly eliminate the impact of spontaneous variations in EEG amplitude. This imperfection reduces the accuracy of cursor movement. The authors evaluated a response verification (RV) procedure in which each outcome is determined by two opposite trials (e.g., one top-target trial and one bottom-target trial). Success, or failure, on both is required for a definitive outcome. The RV procedure reduces errors due to imperfection in intercept selection. Accuracy for opposite-trial pairs exceeds that predicted from the accuracies of individual trials, and greatly exceeds that for same-trial pairs. The RV procedure should be particularly valuable when the first trial has >2 possible targets, because the second trial need only confirm or deny the outcome of the first, and it should be applicable to nonlinear as well as to linear algorithms.

Event-related beta synchronization after wrist, finger and thumb movement

Abstract: Pre-movement event-related desynchronization (ERD) and post-movement event-related synchronization (ERS) were studied in a group of normal subjects during voluntary thumb, index finger and wrist movement. The band power time courses were computed for the upper alpha band (10-12 Hz) and for two frequency bands in the range of beta (16-20 Hz and 20-24 Hz). While a similar mu ERD was found during motor preparation for the 3 movement tasks, significant differences concerning beta synchronization were observed after movement off set. The contralateral percentage beta increase (ERS) was significantly larger in gross movements of the wrist as compared to index finger and thumb movements, which is discussed under the assumption of a cumulative effect. Summarizing, pre-movement desynchronization seems relatively independent of the forthcoming type of movement, whereas the post-movement beta synchronization might depend on the activated muscle mass.

Postmovement Beta Synchronization in Patients With Parkinson's Disease

Abstract: SummaryEvent-related synchronication (ERS) after self-paced, voluntary brisk movement of the right and left thumb was studied in 17 patients with Parkinson's disease (PD) and 17 age-matched control subjects. All patients were receiving L-DOPA and/or DOPA-agonists. The movement-offset-triggered EEG d

On the realization of an analytic high-resolution EEG

Abstract: The analytic solution of the harmonic downward continuation of the scalp potential field in an N-shell heterogeneous, but isotropic, spherical volume conductor model has been derived. The objective of this paper was to investigate the realization of a so-called "high-resolution electroencephalogram (EEG)": by enhancing the poor spatial resolution of EEG recordings. To this end, the forward problem for a dipolar source arbitrarily located at the source point Q=Q(r/sub s/,/spl phi//sub s/,/spl thetav//sub s/) has been determined in a compact matrix notation. It is possible to transfer the potential field given on the outer surface of a spherically shaped volume conductor to an arbitrary inner surface (e.g., to the cortical surface) under consideration of the electrical and geometrical properties of the model. For the application of the proposed method to real-world problems, the coefficients of the series expansion describing the cortical potential distribution are determined by minimizing the squared curvature of the scalp potential field integrated over the scalp surface. Simulation results for distributed sources show that the proposed method is superior to the surface Laplacian method for interelectrode distances below 2.5 cm.

Feature extraction for on-line EEG classification using principal components and linear discriminants

Abstract: The study focuses on the problems of dimensionality reduction by means of principal component analysis (PCA) in the context of single-trial EEG data classification (i.e. discriminating between imagined left- and right-hand movement). The principal components with the highest variance, however, do not necessarily carry the greatest information to enable a discrimination between classes. An EEG data set is presented where principal components with high variance cannot be used for discrimination. In addition, a method based on linear discriminant analysis (LDA), is introduced that detects principal components which can be used for discrimination, leading to data sets of reduced dimensionality but similar classification accuracy.

High-resolution ERD: cortical imaging of event-related desynchronization during motor imagery

Abstract: The objective of this paper is to combine spatially enhanced event-related desynchronization (ERD) data recorded during motor imagery experiments to the anatomy of the brain. Four methods available, the local average reference method (LAR), the surface Laplacian method (LP), the linear estimation method (LE) and the analytical de-blurring method (AD) are applied to the event-related data. All methods yield similar and stable ERD patterns with respect to the location of the maximal desynchronization. The maximal ERD percentage values were found over the contralateral sensorimotor areas of the brain around 625 ms after the presentation of the cue stimulus.

High-Resolution ERD Using Realistic Head Geometry

Abstract: Event-related desynchronization (ERD) of Rolandic mu and beta rhythms is characteristic for a voluntary movement [ 1]. The ERD Starts about 2 s before the actual movement over the contralateral hemisphere and gets bilaterally symmetrical with movement execution [2]. Movement-related magnetic fields measurements showed that the mu-rhythm is mostly of somatosensory origin whereas the beta activity seems to be generated in the primary motor cortex [3). Imagination of movement results in an ERD over the sensorimotor area [4], The aim of this paper is to compare two different highresolution methods, that is the surface Laplacian (SL) and the Linear Estimation (LE) method. The realistic geometry of the subject's head was taken into account. We investigated data recorded during motor imagery of right and left hand movements, respectively.