Literature DB >> 1286147

EEG classification by learning vector quantization.

D Flotzinger1, J Kalcher, G Pfurtscheller.   

Abstract

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.

Mesh:

Year:  1992        PMID: 1286147     DOI: 10.1515/bmte.1992.37.12.303

Source DB:  PubMed          Journal:  Biomed Tech (Berl)        ISSN: 0013-5585            Impact factor:   1.411


  4 in total

1.  Feature extraction for on-line EEG classification using principal components and linear discriminants.

Authors:  K Lugger; D Flotzinger; A Schlögl; M Pregenzer; G Pfurtscheller
Journal:  Med Biol Eng Comput       Date:  1998-05       Impact factor: 2.602

2.  Classification of non-averaged EEG data by learning vector quantisation and the influence of signal preprocessing.

Authors:  D Flotzinger; G Pfurtscheller; C Neuper; J Berger; W Mohl
Journal:  Med Biol Eng Comput       Date:  1994-09       Impact factor: 2.602

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

Authors:  J Kalcher; D Flotzinger; C Neuper; S Gölly; G Pfurtscheller
Journal:  Med Biol Eng Comput       Date:  1996-09       Impact factor: 2.602

4.  Technological Approaches for Neurorehabilitation: From Robotic Devices to Brain Stimulation and Beyond.

Authors:  Marianna Semprini; Matteo Laffranchi; Vittorio Sanguineti; Laura Avanzino; Roberto De Icco; Lorenzo De Michieli; Michela Chiappalone
Journal:  Front Neurol       Date:  2018-04-09       Impact factor: 4.003

  4 in total

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