Distinction Sensitive Learning Vector Quantization Research Articles

Discriminative Common Spatial Pattern Sub-bands Weighting Based on Distinction Sensitive Learning Vector Quantization Method in Motor Imagery Based Brain-computer Interface.

Common spatial pattern (CSP) is a method commonly used to enhance the effects of event-related desynchronization and event-related synchronization present in multichannel electroencephalogram-based brain-computer interface (BCI) systems. In the present study, a novel CSP sub-band feature selection has been proposed based on the discriminative information of the features. Besides, a distinction sensitive learning vector quantization based weighting of the selected features has been considered. Finally, after the classification of the weighted features using a support vector machine classifier, the performance of the suggested method has been compared with the existing methods based on frequency band selection, on the same BCI competitions datasets. The results show that the proposed method yields superior results on "ay" subject dataset compared against existing approaches such as sub-band CSP, filter bank CSP (FBCSP), discriminative FBCSP, and sliding window discriminative CSP.

Imagery of motor actions: Differential effects of kinesthetic and visual–motor mode of imagery in single-trial EEG

Single-trial motor imagery classification is an integral part of a number of brain–computer interface (BCI) systems. The possible significance of the kind of imagery, involving rather kinesthetic or visual representations of actions, was addressed using the following experimental conditions: kinesthetic motor imagery (MIK), visual–motor imagery (MIV), motor execution (ME) and observation of movement (OOM). Based on multi-channel EEG recordings in 14 right-handed participants, we applied a learning classifier, the distinction sensitive learning vector quantization (DSLVQ) to identify relevant features (i.e., frequency bands, electrode sites) for recognition of the respective mental states. For ME and OOM, the overall classification accuracies were about 80%. The rates obtained for MIK (67%) were better than the results of MIV (56%). Moreover, the focus of activity during kinesthetic imagery was found close to the sensorimotor hand area, whereas visual–motor imagery did not reveal a clear spatial pattern. Consequently, to improve motor-imagery-based BCI control, user training should emphasize kinesthetic experiences instead of visual representations of actions.

Frequency component selection for an ECoG-based brain-computer interface.

The aim of the present study was to investigate the most significant frequency components in electrocorticogram (ECoG) recordings in order to operate a brain computer interface (BCI). For this purpose the time-frequency ERD/ERS map and the distinction sensitive learning vector quantization (DSLVQ) are applied to ECoG from three subjects, recorded during a self-paced finger movement. The results show that the ERD/ERS pattern found in ECoG generally matches the ERD/ERS pattern found in EEG recordings, but has an increased prevalence of frequency components in the beta range.

Automated feature selection with a distinction sensitive learning vector quantizer

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 metric with learning. Highest weights are assigned to components in the input vectors which are most informative for classification; non-informative components are discarded. The algorithm is applied to the analyses of multi-channel EEG data and compared with experienced methods.

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

It is well known that μ and central β rhythms start to desynchronize > 1 s before active hand or finger movement. To investigate whether the same cortical areas are involved in desynchronization of μ and central β 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 β ERD was slightly anterior to the focus of μ desynchronization. This can be interpreted that different neural networks are involved in the generation of μ and central β rhythms.

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.