Journal ArticleDOI
Feature extraction for on-line EEG classification using principal components and linear discriminants
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TLDR
A method based on linear discriminant analysis (LDA), is introduced that detects principal components which can be used for discrimination, leading to data sets of reduced dimensionality but similar classification accuracy.Abstract:
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 enable a discrimination between classes. An EEG data set is presented where principal components with high variance cannot be used for discrimination. In addition, a method based on linear discriminant analysis (LDA), is introduced that detects principal components which can be used for discrimination, leading to data sets of reduced dimensionality but similar classification accuracy.read more
Citations
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Journal ArticleDOI
Designing optimal spatial filters for single-trial EEG classification in a movement task
TL;DR: The effectiveness of the devised spatial filters for multi-channel EEG that lead to signals which discriminate optimally between two conditions is demonstrated, and the method's procedural and computational simplicity make it a particularly promising method for an EEG-based brain-computer interface.
Journal ArticleDOI
'Thought' - control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia
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.
Journal ArticleDOI
Current trends in Graz brain-computer interface (BCI) research
Gert Pfurtscheller,Christa Neuper,Christoph Guger,W. Harkam,Herbert Ramoser,Alois Schlögl,B. Obermaier,M. Pregenzer +7 more
TL;DR: This paper describes a research approach to develop a brain-computer interface (BCI) based on recognition of subject-specific EEG patterns using EEG signals recorded from sensorimotor areas during mental imagination of specific movements.
Journal ArticleDOI
Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI)
TL;DR: Experiments resulted in an error rate of 2, 6 and 14% during on-line discrimination of left- and right-hand motor imagery after three days of training and make common spatial patterns a promising method for an EEG-based brain-computer interface.
Journal ArticleDOI
Rapid prototyping of an EEG-based brain-computer interface (BCI)
TL;DR: A new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing is described.
References
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Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
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Pattern Classification and Scene Analysis.
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Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
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Self-Organizing Maps
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
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Pattern recognition and neural networks
Brian D. Ripley,N. L. Hjort +1 more
TL;DR: Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks in this self-contained account.