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Biomedical subjects

J Kalcher

Publications and source records attributed to J Kalcher.

6 recordsLinked to original sources
PubMed1996

Graz brain-computer interface II: towards communication between humans and computers based on online classification of three different EEG patterns.

The paper describes work on the brain--computer interface (BCI). The BCI is designed to help patients with severe motor impairment (e.g. amyotropic lateral sclerosis) to communicate with their environment through wilful modification of their EEG. To establish such a communication channel, two major prerequisites have to be fulfilled: features that reliably describe several distinctive brain states have to be available, and these features must be classified on-line, i.e. on a single-trial basis. The prototype Graz BCI II, which is based on the distinction of three different types of EEG pattern, is described, and results of online and offline classification performance of four subjects are reported. The online results suggest that, in the best case, a classification accuracy of about 60% is reached after only three training sessions. The online results show how selection of specific frequency bands influences the classification performance in single-trial data.

Communication↗
PubMed1996

On-line EEG classification during externally-paced hand movements using a neural network-based classifier.

EEGs of 6 normal subjects were recorded during sequences of periodic left or right hand movement. Left or right was indicated by a visual cue. The question posed was: 'Is it possible to move a cursor on a monitor to the right or left side using the EEG signals for cursor control?' For this purpose the EEG during performance of hand movement was analyzed and classified on-line. A neural network in form of a learning vector quantizertion (LVQ) with an input dimension of 16 was trained to classify EEG patterns from two electrodes and two time windows. After two training sessions on 2 different days, 4 subjects showed a classification accuracy of 89-100%. For two subjects classification was not possible. These results show that in general movement specific EEG-patterns can be found, classified in real time and used to move a cursor on a monitor to the left or right. On-line EEG classification is necessary when the EEG is used as input signal to a brain computer interface (BCI). Such a BCI can be a help for handicapped people.

Adult↗
PubMed1995

Discrimination between phase-locked and non-phase-locked event-related EEG activity.

Differentiation between phase-locked and non-phase-locked event-related EEG activity is an important task in the evaluation of event-related EEG activity. Event-related changes of EEG activity such as event-related desynchronization (ERD) or event-related synchronization (ERS) can be quantified by either instantaneous band power or intertrial variance calculations. In the former case the ERD or ERS can be masked by an event-related potential while in the latter it is not. Examples from sensory stimulation and movement experiments, where the ERD (ERS) is calculated by both methods, are shown and discussed.

Alpha Rhythm↗
PubMed1993

40-Hz oscillations during motor behavior in man.

40-Hz oscillations were measured during finger, toe and tongue movement using an electrode array of 56 electrodes over the pre and post central areas. Each movement was made 150 times in intervals of 12 s. The average power increase in narrow frequency bands between 8 and 40 Hz was then calculated and the topographical distribution studied. 40-Hz oscillations were only found contralateral over the hand area for finger movements, close to the vertex for toe movements and bilateral symmetrical for tongue movements. The maxima of the 40-Hz oscillations occurred before movement onset.

Brain Mapping↗
PubMed1992

EEG classification by learning vector quantization.

EEG classification using Learning Vector Quantization (LVQ) is introduced on the basis of a Brain-Computer Interface (BCI) built in Graz, where a subject controlled a cursor in one dimension on a monitor using potentials recorded from the intact scalp. The method of classification with LVQ is described in detail along with first results on a subject who participated in four on-line cursor control sessions. Using this data, extensive off-line experiments were performed to show the influence of the various parameters of the classifier and the extracted features of the EEG on the classification results.

Algorithms↗