Biomedical subjects
Publications and source records attributed to C Neuper.
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.
In assuming functional differences between different EEG alpha frequency bands, recent studies emphasize the importance of using narrow (8-10 Hz or 10-12 Hz) instead of broad alpha frequency ranges (8-12 Hz). Due to individual differences in alpha activity, it has also been suggested to adjust alpha frequency bands individually for each participant. The present paper highlights the dissociating role of different task demands on the extent of event-related desynchronization (ERD) in different alpha frequency bands. In analyzing the data of four large-scale EEG studies (with sample sizes of 51, 58, 55, and 66, respectively) employing a wide range of cognitive tasks, we found evidence that the correlations between lower and upper alpha band ERD systematically decline as task demands increase.
We investigated the behavior of short-lasting beta bursts (beta rebound, beta ERS) induced after imagination of hand, foot or tongue movement. Nine able-bodied subjects were asked to imagine one type of movement following the presentation of a visual cue stimulus. EEG was recorded from 60 closely spaced electrodes placed over frontal, central and parietal areas. Spatiotemporal ERD/ERS patterns were evaluated in form of time-frequency maps. Seven out of nine subjects displayed a significant short-lasting beta power increase (beta rebound) after termination of imagery of movement of both feet in the frequency range of 23-29Hz with a clear maximum at the vertex. In contrast, only two subjects displayed a clear beta rebound at the vertex after imagery of one of the hands, although five exhibited a significant beta rebound at the contralateral side of the head. None showed a beta rebound after tongue motor imagery. The midcentrally located beta rebound is interpreted as an electrophysiological correlate of a simultaneous "resetting" of overlapping neural networks in the foot representation areas and the SMA.
OBJECTIVES: The objective of the paper was the determination of electrical brain activity propagation in sensorimotor areas during hand movement imagery. METHODS: Right-hand and left-hand movement imagination was studied in three subjects. The 10-channel Multivariate Autoregressive Model (MVAR) was fitted to EEG signals recorded from subsets of electrodes overlying central and related brain areas. By means of the Short-time Directed Transfer Function (SDTF) the propagation of brain activity as a function of frequency and time was found. RESULTS: During imagery the relation between propagations in gamma and beta bands changed significantly for electrodes overlying sensorimotor areas, namely the increase in gamma was accompanied by the decrease in the beta band. CONCLUSIONS: The hypothesis was put forward that these kinds of changes in flow of electrical brain activity are connected with the specific information processing.
In this study we analyzed the influence of the personality dimension extraversion-introversion (E) on the level and topographical distribution of cortical activation. In 62 participants (32 introverts and 30 extraverts), we measured the extent of Event-Related Desynchronization (ERD) in the EEG during performance of a short-term memory (i.e., temporary maintenance of information) and a more complex working memory task (i.e., temporary maintenance and active manipulation of information). The results indicate that during performance of both tasks, introverts display a larger amount of ERD than extraverted individuals. Moreover, the present E effects largely match previous studies as to the restriction of these effects to lower EEG frequency ranges (approx. 4-8 Hz). Topographical analyses show that the E effects are primarily present over (right-hemispheric) frontal and parietal regions of the cerebral cortex.
Starting from the well-established finding that brighter individuals display a more efficient brain function when performing cognitive tasks (i.e., neural efficiency), we investigated the relationship between intelligence and cortical activation in the context of working memory (WM) tasks. Fifty-five male (n=28) and female (n=27) participants worked on (1) a classical forward digit span task demanding only short-term memory (STM), (2) an attention-switching task drawing on the central executive (CE) of WM and (3) a WM task involving both STM storage and CE processes. During performance of these three types of tasks, cortical activation was quantified by the extent of Event-Related Desynchronization (ERD) in the alpha band of the human EEG. Correlational analyses revealed associations between the amount of ERD in the upper alpha band and intelligence in several brain regions. In all tasks, the males were more likely to display the negative intelligence-cortical activation relationship. Furthermore, stronger associations between ERD and intelligence were found for fluid rather than crystallized intelligence. Analyses also point to topographical differences in neural efficiency depending on sex, task type and the associated cognitive subsystems engaged during task performance.
Event-related alpha band desynchronization is frequently used to analyze spatiotemporal cortical activation patterns during the performance of cognitive tasks. In the present paper the sensitivity of alpha band desynchronization to increasing levels of cognitive load and to different cognitive working memory components is investigated. A 27-channel electroencephalogram of 62 participants while solving (a) a short-term memory and (b) a working memory task (dual task), each with five levels of memory load, was analyzed. We found (a) a linearly increasing desynchronization in the upper alpha band with ascending cognitive load, and (b) evidence of the involvement of distinguishable cognitive components (storage and controlled attention) in the memory tasks.
Event-related beta electroencephalographic (EEG) changes were studied during wrist movements induced by functional electrical stimulation (FES) of the appropriate forearm muscles in healthy volunteers. Active and passive hand movements were investigated as control conditions. Significant EEG changes with respect to a pre-movement period were analyzed by calculating time-frequency maps of event-related (de-)synchronization (ERD/ERS) for 32 EEG channels recorded from sensorimotor and premotor areas. Immediately after the beginning of the FES movement, a prominent ERD was found, followed by a beta ERS similar to that observed after active or passive wrist movements. Both changes were maximal over the contralateral primary hand area. The main difference between active and stimulation-induced movements was that in the latter case no ERD was detectable prior to movement-onset. These findings suggest that the sensorimotor processing during FES involves some of the processes which are also involved in voluntary hand movements.
OBJECTIVE: This case study describes how a completely paralyzed patient, diagnosed with severe cerebral palsy, was trained over a period of several months to use an electroencephalography (EEG)-based brain-computer interface (BCI) for verbal communication. METHODS: EEG feedback training was performed in the patient's home (clinic), supervised from a distant laboratory with the help of a 'telemonitoring system'. Online feedback computation was based on single-trial analysis and classification of specific band power features of the spontaneous EEG. Task-related changes in brain oscillations over the course of training steps was investigated by quantifying time-frequency maps of event-related (de-)synchronization (ERD/ERS). RESULTS: The patient learned to 'produce' two distinct EEG patterns, beta band ERD during movement imagery vs. no ERD during relaxing, and to use this for BCI-controlled spelling. Significant learning progress was found as a function of training session, resulting in an average accuracy level of 70% (correct responses) for letter selection. 'Copy spelling' was performed with a rate of approximately one letter per min. CONCLUSIONS: The proposed BCI training procedure, based on electroencephalogram (EEG) biofeedback and concomitant adaptation of feature extraction and classification, may improve actual levels of communication ability in locked-in patients. 'Telemonitoring-assisted' BCI training facilitates clinical application in a larger number of patients.
This paper describes a paralyzed patient diagnosed with severe infantile cerebral palsy, trained over a period of several months to use an EEG-based brain-computer interface (BCI) for verbal communication. The patient learned to "produce" two distinct EEG patterns by mental imagery and to use this skill for BCI-controlled spelling. The EEG feedback training was conducted at a clinic for Assisted Communications, supervised from a distant laboratory with the help of a telemonitoring system. As a function of training sessions significant learning progress was found, resulting in an average accuracy level of 70% correct responses for letter selection. At present, "copy spelling" can be performed with a rate of approximately one letter per minute. The proposed communication device, the "Virtual Keyboard", may improve actual levels of communication ability in completely paralyzed patients. "Telemonitoring-assisted" training facilitates clinical application in a larger number of patients.
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.
Induced beta oscillations were studied in three normal subjects after mechanical stimulation of each fingertip of the right hand. For stimulation short tactile stimuli of 10 ms duration were presented at intervals of 4 s. The electroencephalogram was recorded with 60 closely spaced electrodes and sampled at 256 Hz. For each subject the reactive beta band was determined by comparing 1-s power spectra. The largest beta power increase was found in the range of 12-28 Hz on electrodes overlying the left-hand area. The Matching Pursuit algorithm was applied to single trials data and the frequency of single "atoms" determined. In one subject the little finger showed a significant higher frequency as compared to the thumb. The difference in frequency was in the order of 1 Hz.
A new method is presented for the analysis of event-related EEG phenomena, in particular event related desynchronisation (ERD) and event related synchronisation (ERS) related to a voluntary movement; the method offers: high time-frequency resolution and, hence, increased ERD/ERS sensitivity (especially in the gamma band, where improvement can exceed an order of magnitude); the ability to analyse the whole picture of energy changes at once, without setting a priori the analysed frequency bands; and a parametric description of the signal's structures. The main idea is based upon averaging energy distributions of single EEG trials in the time-frequency plane. As the estimator for the signal's energy density, matching pursuit is chosen, with stochastic Gabor dictionaries. Other possible estimates are presented on a simulated signal and discussed briefly. The consistency of the results with previous findings is evaluated on the data from a classical voluntary finger movement experiment.
Oscillations in the alpha and beta band (<35 Hz) display a dynamic behavior and show characteristic spatiotemporal patterns in sensory, motor and cognitive tasks. The event-related desynchronization (ERD) of alpha band and beta rhythms can be seen as a correlate of an activated cortical area with an increased excitability level of neurons. An event-related synchronization (ERS) of frequency components between 10 and 13 Hz may represent a deactivated cortical area or inhibited cortical network, at least under certain circumstances. It is hypothesized, that antagonistic ERD/ERS patterns, called 'focal ERD/surround ERS', may reflect a thalamo-cortical mechanism to enhance focal cortical activation by simultaneous inhibition of other cortical areas. Induced oscillations in the beta band (13-35 Hz, beta ERS) were found in sensorimotor areas after voluntary movement and after somatosensory stimulation. This may be interpreted as a state of 'inhibition' of neural circuitry in the primary motor cortex. Simultaneous activation of the motor cortex by e.g. motor imagery lead to an attenuation of the beta ERS. Moreover, there is evidence that the frequency of the induced beta oscillations represent a 'resonance-like frequency' of underlying cortical networks. However, further research is needed to investigate the functional meaning of bursts of beta oscillations below 35 Hz.
OBJECTIVE: We studied event-related synchronization (ERS) of beta rhythms related to voluntary movement vs. stimulation of upper and lower limbs. The aim of this study was to investigate whether the frequency of the beta response is related to specific regions within the sensorimotor strip. METHODS: Self-paced movement and electrical stimulation of the dominant hand and foot/leg was investigated in 10 right-handed volunteers. The electroencephalogram was recorded from closely spaced electrodes over central areas and processed time-locked to movement-offset or stimulation. In order to identify the dominant frequency of the induced beta oscillations, time-frequency maps were calculated using the continuous wavelet transformation. For the specific beta frequency bands, the band power time courses were analyzed by quantifying the event-related (de-)synchronization (ERD/ERS). RESULTS: Both limb movement and somatosensory stimulation induced bursts of beta oscillations appearing within 1 s after movement/stimulation with a clear focus close to the corresponding sensorimotor representation area. The peak frequency was significantly lower over the hand area (below approximately 20 Hz) than at mid-central sites overlying the foot representation area (above approximately 20 Hz). But no difference was found between movement and stimulation of the respective limb. CONCLUSIONS: Analyzing the frequency of induced beta activity revealed concomitant oscillations at slightly different frequencies over neighboring cortical areas. These oscillations might be indicative for a resonance-like behavior of connected sub-networks in sensorimotor areas.
Short mechanical stimulation of the fingertip was studied in 10 subjects using 34 closely spaced EEG electrodes overlying sensorimotor areas. The tactile skin stimulation device consisted of a dot matrix printer head. Each stimulus lasted 10 msec with an interstimulus interval of 4 seconds. Tactile stimulation induced beta oscillations, which were largest in the 16 to 20-Hz band, with a maximum centered within 1 second after stimulation. These beta oscillations were found over both hemispheres with a clear dominance over the contralateral hand representation area.
The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional digital signal processor (DSP) board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive (AAR) model and linear discriminant analysis (LDA).
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.