scholarly journals Optimal spatial filtering of single trial EEG during imagined hand movement

2000 ◽  
Vol 8 (4) ◽  
pp. 441-446 ◽  
Author(s):  
H. Ramoser ◽  
J. Muller-Gerking ◽  
G. Pfurtscheller
Keyword(s):  
Hand Movement ◽  
Spatial Filtering ◽  
Single Trial

scholarly journals Temporal and Spatial Features of Single-Trial EEG for Brain-Computer Interface

2007 ◽  
Vol 2007 ◽  
pp. 1-14 ◽  
Author(s):  
Qibin Zhao ◽  
Liqing Zhang
Keyword(s):  
Brain Computer Interface ◽  
Hand Movement ◽  
Event Related Potentials ◽  
Computer Interface ◽  
Single Trial ◽  
Spatial Filters ◽  
Classification Rate ◽  
Temporal Features ◽  
Related Potentials

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.

scholarly journals Prediction of Hand Movement Speed and Force from Single-trial EEG with Convolutional Neural Networks

2018 ◽  
Author(s):  
Ramiro Gatti ◽  
Yanina Atum ◽  
Luciano Schiaffino ◽  
Mads Jochumsen ◽  
José Biurrun Manresa
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hand Movement ◽  
Substantial Improvement ◽  
Movement Speed ◽  
Single Trial ◽  
Eeg Signals ◽  
Brain Signals ◽  
Electroencephalogram Eeg

AbstractBuilding accurate movement decoding models from brain signals is crucial for many biomedical applications. Decoding specific movement features, such as speed and force, may provide additional useful information at the expense of increasing the complexity of the decoding problem. Recent attempts to predict movement speed and force from the electroencephalogram (EEG) achieved classification accuracy levels not better than chance, stressing the demand for more accurate prediction strategies. Thus, the aim of this study was to improve the prediction accuracy of hand movement speed and force from single-trial EEG signals recorded from healthy volunteers. A strategy based on convolutional neural networks (ConvNets) was tested, since it has previously shown good performance in the classification of EEG signals. ConvNets achieved an overall accuracy of 84% in the classification of two different levels of speed and force (4-class classification) from single-trial EEG. These results represent a substantial improvement over previously reported results, suggesting that hand movement speed and force can be accurately predicted from single-trial EEG.

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