The development of an EEG-based brain-computer interface (BCI) requires rapid and reliable discrimination of EEG patterns, e. g., associated with motor imagery. One sided hand movement imagination results in EEG changes located at contra- and ipsilateral central areas. We demonstrate that spatial filters for multi-channel EEG effectively extract discriminatory information from two populations of single-trial EEG, recorded during left and right hand movement imagery. The best classification results for three subjects are 90.8%, 92.7%, and 99.7%. The spatial filters are estimated from a set of data by the method of Common Spatial Patterns and reflect the specific activation of cortical areas. The method performs a weighting of the electrodes according to their importance for the classification task. The high recognition rates and computational simplicity make it a promising method for an EEG-based brain-computer interface.

We devise spatial filters for multi-channel EEG that lead to signals which discriminate optimally between two conditions. We demonstrate the effectiveness of this method by classifying single-trial EEGs, recorded during preparation for movements of left or right index finger or right foot. Best classification rates for 3 subjects were 94%, 90% and 84%, respectively. The filters are estimated from a set of multichannel EEG data by the method of Common Spatial Patterns, and reflect the selective activation of cortical areas. By construction, we obtain an automatic weighting of electrodes according to their importance for the classification task. Computationally, this method is parallel by nature, and demands only the evaluation of scalar products. So it is well suited for on-line data processing. The recognition rates obtained with this relatively simple method are as good or higher than those obtained previously with other methods. The high recognition rates and the method's procedural ...

What is Quantitative EEG- Kaiser- 1 Basic description of quantitative electroencephalography (EEG) in the context of neurotherapeutic application. Issues associated with spectral analysis of human EEG are discussed and an example quantitative EEG assessment report is provided.

OF THESIS ANALYSIS OF LVQ IN THE CONTEXT OF SPONTANEOUS EEG SIGNAL CLASSIFICATION Learning Vector Quantization (LVQ) has proven to be an effective classification procedure. Since its introduction by Kohonen in 1990 several extensions to the basic algorithm have been proposed. This paper investigates what and how LVQ learns in the context of EEG signal classification. LVQ is shown to be comparable with other Neural Network algorithms for the task of classifying electroencephalograph (EEG) signals, yielding approximately 80% classification accuracy for three out of the four subjects tested when differentiating between two different mental tasks. The best classification accuracy was obtained with unnormalized, sixth-order autoregressive, AR(6), coefficients derived from raw, unfiltered EEG signals. The LVQ2.1 algorithm outperformed any of the other traditional LVQ algorithms tested, yielding a slightly higher classification accuracy than the LVQ3 algorithm. The highest classification accu...