Discrete-time signal processing
Discrete-time signal processing
Do Bounded Signals Have Bounded Amplitudes?
Multidimensional Systems and Signal Processing - Special issue on recent developments in time-frequency analysis
Local Neural Classifier for EEG-Based Recognition of Mental Tasks
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
An introduction to variable and feature selection
The Journal of Machine Learning Research
EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface
International Journal of Software Science and Computational Intelligence
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To allow motor-disabled people for communication, Brain-Computer Interfaces (BCIs) are being developed. Such a communication does not depend on the brain's normal output pathways of peripheral nerves and muscles, but is based on analysis of recorded brain activity. In this paper, we compare the performance of power features and phase locking values (PLVs) computed from broadband and narrowband filtered EEG signals for discriminating 3 mental tasks in the framework of a BCI. EEG signals were recorded from 5 subjects while performing the 3 mental tasks left- and right-hand movement imagination and word generation. To reduce the total amount of features, the most discriminative features were selected in a 2-step feature selection procedure by SVM-based recursive feature elimination.Significance tests demonstrated that band power features were more discriminative when they were computed in the narrower frequency band 8-12 Hz. In case of PLV features, the discrimination of mental tasks was significantly better when they were computed from the broader 8-30 Hz frequency band, as compared to the narrower bands 8-12, 13-18 and 19-30 Hz.