Biomedical subjects

M Pregenzer

Publications and source records attributed to M Pregenzer.

4 recordsLinked to original sources

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↗

PubMed1994

Visualization of sensorimotor areas involved in preparation for hand movement based on classification of mu and central beta rhythms in single EEG trials in man.

It is well known that mu and central beta rhythms start to desynchronize > 1 s before active hand or finger movement. To investigate whether the same cortical areas are involved in desynchronization of mu and central beta rhythms, 56-channel EEG recordings were made during right- and left-finger flexions in three normal subjects. The event-related desynchronization (ERD) was quantified in single EEG trials and classified by the Distinction Sensitive Learning Vector Quantization (DSLVQ) algorithm. This DSLVQ selects the most relevant features (electrode positions) for discrimination between the preparatory state for left- and right-finger movements. It was found that the most important electrode positions were close to the primary hand area. However, in all three subjects the focus of the central beta ERD was slightly anterior to the focus of mu desynchronization. This can be interpreted that different neural networks are involved in the generation of mu and central beta rhythms.

Electroencephalography↗

PubMed1994

Selection of electrode positions for an EEG-based brain computer interface (BCI).

One major question in designing an EEG-based Brain Computer Interface to bypass the normal motor pathways is the selection of proper electrode positions. This study investigates electrode selection with a Distinction Sensitive Learning Vector Quantizer (DSLVQ). DSLVQ is an extended Learning Vector Quantizer (LVQ) which employs a weighted distance function for dynamical scaling and feature selection. The data analysed and classified were 56-channel EEG recordings over sensorimotor areas during preparation for discrete left or right index finger flexions. Data from 3 subjects are reported. It was found by DSLVQ that the most important electrode positions for differentiation between planning of left and right finger movement overlie cortical finger/hand areas over both hemispheres.

Adult↗

PubMedDate not supplied

EEG-based brain computer interface (BCI). Search for optimal electrode positions and frequency components.

Several laboratories around the world have recently started to investigate EEG-based brain computer interface (BCI) systems in order to create a new communication channel for subjects with severe motor impairments. The present paper describes an initial evaluation of 64-channel EEG data recorded while subjects used one EEG channel over the left sensorimotor area to control on-line vertical cursor movement. Targets were given at the top or bottom of a computer screen. Data from 3 subjects in the early stages of training were analyzed by calculating band power time courses and maps for top and bottom targets separately. In addition, the Distinction Sensitive Learning Vector Quantizer (DSLVQ) was applied to single-trial EEG data. It was found that for each subject there exist optimal electrode positions and frequency components for on-line EEG-based cursor control.

Adult↗