Martin Pregenzer

• University of Innsbruck

This paper describes a research approach to develop a brain-computer interface (BCI) based on recognition of subject-specific EEG patterns. EEG signals recorded from sensorimotor areas during mental imagination of specific movements are classified on-line and used e.g. for cursor control. In a number of on-line experiments, various methods for EEG...

Electroencephalogram (EEG) signals, which reflect the electrical activity of the brain, can be classified on a single trial basis. An internally or externally paced event results not only in the generation of an event-related potential (ERP), but also in a change of the ongoing bioelectrical brain activity in form of either an event-related desynch...

A new communication channel for severely handicapped people could be opened with a direct brain to computer interface (BCI). Such a system classifies electrical brain signals online. In a series of training sessions, where electroencephalograph (EEG) signals are recorded on the intact scalp, a classifier is trained to discriminate a limited number...

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 en...

Three subjects were asked to imagine either right or left hand movement depending on a visual cue stimulus. The interval between two consecutive imagination tasks was > 10 s. Each subject imagined a total of 160 hand movements in each of 3-4 sessions (training) without feedback and 7-8 sessions with feedback. The EEG was recorded bipolarly from lef...

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 ana...

An extended version of Kohonen's Learning Vector Quantization (LVQ) algorithm, called Distinction Sensitive Learning Vector Quantization (DSLVQ), is introduced which overcomes a major problem of LVQ, the dependency on proper pre-processing methods for scaling and feature selection. The algorithm employs a weighted distance function and adapts the m...

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 sen...

This paper describes a simple but very powerful method for feature selection. The Distinction Sensitive Learning Vector Quantizer (DSLVQ) is a learning classifier which focuses on relevant features according to its own instance based classifications. Two different experiments describe the application of DSLVQ as a feature selector for an EEG-based...

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...

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 f...

Ein Hauptproblem bei der Entwicklung eines auf EEG-Ableitungen basierenden Brain Computer Interfaces (BCI) zur Umgehung natũirlicher, motorischer Signalwege ist die Auswahl von geeigneten Elektrodenpositionen. Dieser Artikel beschreibt die Elektrodenauswahl mit Hilfe des Distinction Sensitive Learning Vector Quantizers (DSLVQ). DSLVQ ist ein verbes...

A Distinction Sensitive Learning Vector Quantizer (DSLVQ), based on the LVQ3 algorithm, is introduced which automatically adjusts the influence of the input features according to their observed relevance for classification. DSLVQ is less sensitive to noisy features than standard LVQ and its importance adjustments are transparent and can be exploite...

Two feature selection methods, a distinction-sensitive learning vector quantizer (DSLVQ) and a genetic algorithm (GA) approach, are applied to multichannel electroencephalogram (EEG) patterns. It is shown how DSLVQ adjusts the influence of different input features according to their relevance for classification. Using a weighted distance function D...

Inverse filtering can be used to identify transient events. Often, artefacts in the EEG are such transient events. Sleep EEG data of 8 eight European sleep labs were scored in 1s-epochs for 9 types of artefacts. The area under the ROC curve (AUC) was 0.857 and 0.898 for muscle and movement artefacts, respectively. Kalman filtering can be used to es...