Showing papers by "Gert Pfurtscheller published in 2003"
TL;DR: The aim of the present study was to demonstrate the first time the non-invasive restoration of hand grasp function in a tetraplegic patient by electroencephalogram (EEG)-recording and functional electrical stimulation (FES) using surface electrodes.
542 citations
28 Jul 2003
TL;DR: Ninety-nine healthy people participated in a brain-computer interface (BCI) field study conducted at an exposition held in Graz, Austria, and nearly 93% of the subjects were able to achieve classification accuracy above 60% after two sessions of training.
Abstract: Ninety-nine healthy people participated in a brain-computer interface (BCI) field study conducted at an exposition held in Graz, Austria. Each subject spent 20-30 min on a two-session BCI investigation. The first session consisted of 40 trials conducted without feedback. Then, a subject-specific classifier was set up to provide the subject with feedback, and the second session - 40 trials in which the subject had to control a horizontal bar on a computer screen - was conducted. Subjects were instructed to imagine a right-hand movement or a foot movement after a cue stimulus depending on the direction of an arrow. Bipolar electrodes were mounted over the right-hand representation area and over the foot representation area. Classification results achieved with 1) an adaptive autoregressive model (39 subjects) and 2) band power estimation (60 subjects) are presented. Roughly 93% of the subjects were able to achieve classification accuracy above 60% after two sessions of training.
511 citations
TL;DR: 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.
442 citations
TL;DR: 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.
338 citations
28 Jul 2003
TL;DR: Relevant clinical applications of BCI-based systems for control of a virtual keyboard device and operations of a hand orthosis are reported and it is demonstrated how information transfer rates can be acquired by real time classification of oscillatory activity.
Abstract: 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.
298 citations
TL;DR: The induced oscillations (ERS) are dominant in the 10‐ to 13‐Hz band and very likely mediated by thalamic gating.
Abstract: The phenomena of event-related desynchronization (ERD) and synchronization (ERS) reflect the dynamics of neural networks and can be observed on different scalp locations at the same moment of time. Whereas on one cortical area a focal 10-Hz ERD can be found, other areas can display a 10-Hz ERS. This phenomenon is called focal ERD/surround ERS and is interpreted as a correlate of an activated cortical area (ERD) and simultaneously deactivated or inhibited other areas. The induced oscillations (ERS) are dominant in the 10- to 13-Hz band and very likely mediated by thalamic gating.
204 citations
01 Dec 2003
TL;DR: A "virtual keyboard" (VK) is a letter spelling device operated by spontaneous electroencephalogram (EEG) whereby the EEC is modulated by mental hand and leg motor imagery.
Abstract: 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.
175 citations
TL;DR: The findings suggest that the sensorimotor processing during FES involves some of the processes which are also involved in voluntary hand movements.
146 citations
TL;DR: Analysis of 4 young paraplegic patients' last 2 experimental sessions showed that the trial length can be reduced to values around 2 s to obtain the highest possible information transfer.
Abstract: The "Graz Brain-Computer Interface (BCI)" transforms changes in oscillatory EEG activity into control signals for external devices and feedback. These changes are induced by various motor imageries performed by the user. For this study, 2 different types of motor imagery (movement of the right vs. left hand or both feet) were classified by processing 2 bipolar EEG-channels (derived at electrode positions C3 and C4). After a few sessions, within some weeks, 4 young paraplegic patients learned to control the BCI. In accordance with the participants, decision-speed (trial length) was varied and the information transfer rate (ITR) was calculated for each run. All experimental runs have been feedback-runs employing a simple computer-game-like paradigm. A falling ball had to be led into a randomly marked target halfway down the screen. The horizontal position was controlled by the BCI-output signal and the trial length was varied by the investigator across runs. The goal was to find values for trial length enabling a maximum ITR. Three out of 4 participants had good results after a few runs. Analysis of their last 2 experimental sessions, each containing between 10 and 16 runs, showed that the trial length can be reduced to values around 2 s to obtain the highest possible information transfer. Attainable ITRs were between 5 and 17 bit/min depending on the participant's performance and condition.
95 citations
TL;DR: It is shown that the induced midcentral beta oscillations following movement-offset display not only slightly higher frequency components, but have also a significantly earlier onset.
71 citations
15 Sep 2003
TL;DR: The findings show that the detection of event-related desynchronization and synchronization in ECoG data can be used to reliably provide switch control directly by the brain and is therefore very suitable as the basis of a direct brain interface.
Abstract: Adaptive autoregressive parameters and a linear classifier were used to detect movement related desynchronization and synchronization patterns in single-channel electrocorticogram (ECoG) obtained from implanted electrode grids. The best classification accuracies found had more than 90% hits and less than 10% false positives. The findings show that the detection of event-related desynchronization and synchronization in ECoG data can be used to reliably provide switch control directly by the brain and is therefore very suitable as the basis of a direct brain interface.
28 May 2003
TL;DR: The implementation of a telemonitoring system is described, which makes it possible for the developer to control and supervise the BCI training from his or her own place of work.
Abstract: By the use of a brain-computer interface (BCI), it is possible for completely paralyzed patients, who have lost their ability to speak, to have a new possibility to communicate with their environment. The training with such a BCI system can be performed at the patient's home, if there is a responsible person present who is familiar with the system. This person has to adjust different parameters and to adapt the training individually to each patient. Since this function is usually taken over by the developers of the system, the number of patients who can be included in regular BCI training is restricted due to geographical distances. This paper describes the implementation of a telemonitoring system, which makes it possible for the developer to control and supervise the BCI training from his or her own place of work. First experiences with a patient living far away from the developer's lab are reported.
[...]
01 Jan 2003
TL;DR: One characteristic feature of the brain is its ability to generate rhythmic potentials or oscillatory activity, which can result in phasic changes in the synchrony of cell populations due to externally or internally paced events and lead to characteristic EEG patterns.
Abstract: One characteristic feature of the brain is its ability to generate rhythmic potentials or oscillatory activity. Already in 1949 Jasper and Penfield discovered this fact and discussed the relationship between alpha and beta rhythms and their functioning in relation to underlying neural networks. The frequency of brain oscillations depends both on membrane properties of single neurons and the organization and interconnectivity of networks to which they belong (Lopes da Silva, 1991). Such a network can either comprise a large number of neurons controlled, for example, by thalamo-cortical feedback loops or only a small number of neurons interconnected, for example, by intra-cortical feedback loops. Coherent activity in large neuronal pools can result in high amplitude and low frequency oscillations (e. g. alpha band rhythms), whereas synchrony in localized neuronal pools can be the source of gamma oscillations (Lopes da Silva and Pfortscheller, 1999). The dynamic of such a network can result in phasic changes in the synchrony of cell populations due to externally or internally paced events and lead to characteristic EEG patterns. Two such pattern types are observed, the event-related desynchronization, or ERD, in form of an amplitude attenuation and the event-related synchronization, or ERS, in form of an enhancement of specific frequency components (Pfortscheller and Lopes da Silva, 1999a, Pfortscheller and Lopes da Silva, 1999b).
16 Sep 2003
TL;DR: In this paper, a review of brain-computer communication based on motor imagery and the dynamics of brain oscillations is presented, and the concept of motor imagery as experimental strategy and two different modes of operation a brain computer interface can have are explained.
Abstract: 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.
TL;DR: In this paper, the most significant frequency components in electrocorticogram (ECoG) recordings in order to operate a brain computer interface (BCI) were investigated.
Abstract: 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.
20 Mar 2003
TL;DR: A direct brain interface (DBI) based on the detection of event-related potentials (ERPs) in human electrocorticogram (ECoG) is under development and several opportunities for improved detection accuracy have been identified.
Abstract: A direct brain interface (DBI) based on the detection of event-related potentials (ERPs) in human electrocorticogram (ECoG) is under development. Accurate detection has been demonstrated with this approach (near 100% on a few channels) using a single-channel cross-correlation template matching (CCTM) method. Several opportunities for improved detection accuracy have been identified. Detection using a multiple-channel CCTM method and a variety of detection methods that take advantage of the simultaneous occurrence of ERPs and event-related desynchronization/synchronization (ERD/ERS) have been demonstrated to offer potential for improved detection accuracy.
20 Mar 2003
TL;DR: The findings show that the detection of movement-related patterns in ECoG data can be used to reliably provide switch control directly by the brain and is therefore very suitable as the basis of a direct brain interface.
Abstract: Wavelet packet analysis and a genetic algorithm were used to detect movement-related patterns in single channel electrocorticogram (ECoG). Detection accuracies of more than 90% hits and less than 10% false positives were found. The findings show that the detection of movement-related patterns in ECoG data can be used to reliably provide switch control directly by the brain and is therefore very suitable as the basis of a direct brain interface.
TL;DR: In this study, statistical pattern recognition method based on AR model was introduced to discriminate the EEG signals recorded during right and left motor imagery and the effectiveness of the method was confirmed through the experimental studies.
01 Jan 2003
TL;DR: In this article, a statistical pattern recognition method based on AR model was introduced to discriminate the electroencephalograph (EEG) signals recorded during right and left motor imagery And learning methods were investigated Also, correlation between C3 and C4 signals were investigated, and thereby which AR (combine AR or multivariable AR) model must be used in each EEC recording method
Abstract: In this paper, statistical pattern recognition method based on AR model was introduced to discriminate the electroencephalograph (EEG) signals recorded during right and left motor imagery And learning methods were investigated Also, correlation between C3 and C4 signals were investigated, and thereby which AR (combine AR or multivariable AR) model must be used in each EEC recording method
TL;DR: 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, learns to "produce" two distinct EEG patterns by mental imagery and to use this skill for BCI-controlled spelling.
Abstract: 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.
TL;DR: The first results of a copy spdlint are presented: the enhancement of the Graz-BCI and the transition lo the asynchronous opcTahon mode enables an incrcase in the v o m i m m m i v / a t i o n speed.
Abstract: M ΜΜΛΚΥ Nowudays a varicty l Brain-Computer Ι ι ι ΐ ι Ί ΐ . κ ι · dU 'h hascil conuiuinication Systems are . i \\ . i i l , i bk · . Λ common problcm, however, is ihc Iow i n i o n i K i t m n hausier rale from Ihe human brain to the m.k-hmc. The enhancement ΐ ) Γ the Graz-BCI in order to h .nu l le > elasses and the transition lo the asynchronous opcTahon mode enables an incrcase in the v o m i m m i v / a t i o n speed. The first results of a copy spdlint: e\\peri inent demonstrate a considerahle increase i n spei 11 n«: performanee comparcd to the previous s\\ sie m.
TL;DR: This second pari is focused on the Implementat ion of the BCI, where the tetraplegic patient was able to generate bursts of beta oscillations in thc EEG and be able to control bis izrasp l imction reliably by "thoughls".
Abstract: M \\ I M . \\ K V I h c ;iim of thc prcsenl study was to domonst ra ic lor ihe First timc thc non-invasive K's toui i ion h and grasp funetion in a tctraplegic p.ihcnt In lunclional clcctrical Stimulation (FES) using M I I | ; U C clcclrodcs controllcd via an oLvtrocnccpruilogram ( EEG)-based Brain-Computer I n t c i lacc (BCI) . This second pari is focused on the Implementat ion of the BCI. The tetraplegic patient was able to generate bursts of beta oscillations in thc EEG In imagmalion of foot movements. These beta-bursts \\\\ere analy/cd and elassified by the Brain-Computer In te r l aee and a switeh signal was generated for control öl the l· l· S de vice. The patient was able to control bis izrasp l imction reliably by \"thoughls\".
TL;DR: The latencies of desynchrom/ation and resynchronization peaks were significant ly larger in the eyes opened condition compared with eyes elosed and the amount of event related ulcisynchroni/.ation ealculated.
Abstract: s i ' M M A K Y : Ihc (de)synchwnization of alpha oscillat ions was studicd in len normal subjects using a closed loop approach. Visual Stimulation was performed under tu o dihcrcnt conditions, eyes opened and eyes closed. l he l l ( i was recorded over the occipital area and samplcd ti t 100 H/. Data was processed in real-time and ovalua icd online. Triggered by alpha bandpower in the 7 to l Λ H/ band, short red light flashes of 10 ms duration \\\\cre presented at intervals of at least 2 s. This led to a des\
chroni/.ation with following resynchronization of alpha oscillations. Trials were controlled for artifacts, axeraged offline and the amount of event related ulcisynchroni/.ation ealculated. The latencies of desynchrom/ation and resynchronization peaks were significant ly larger in the eyes opened condition s compared with eyes elosed.
TL;DR: The goal of this paper is to demonstrate the first time a combined HRV-EEG monitoring in neonates with very low birth weight (VLBW).
Abstract: l lcart rate variability ( I IRV) is frequently used to study hean rate regulation and therewith especially the lunct ion l thc brain slem [1,2]. Previous studies have mdicated that the Cerebral Function Analysis Monitor ( (ΤΑΜ) is a powerful tool to evaluate continuously cerebral funetion in the Neonatal Intensive Gare Unit 1 3. 4.5 1 . Λ combination of both, HRV and clcctroencephalogram (EEG) measurements are thercfore suitable to monitor at the same time the functioning of cortical and brain stem structures [6]. The goal of this paper is to demonstrate the first time a combined HRV-EEG monitoring in neonates with very low birth weight (VLBW).