Fast feature selection to compare broadband with narrowband phase synchronization in brain-computer interfaces

View/ Open
Author(s)
Gysels, E.
Renevey, P.
Celka, Patrick
Griffith University Author(s)
Year published
2007
Metadata
Show full item recordAbstract
OBJECTIVE: Brain-computer interface (BCI) research aims at developing communication devices for the motor disabled. Such devices are not driven by muscle activity, but by brain activity recorded during different mental tasks. We present here the comparison of phase synchronization and power spectral density (PSD) features, computed from broadband and narrowband filtered EEG signals and their ability to discriminate three mental tasks. METHODS: EEG signals were recorded from five subjects while performing left and right hand movement imagination and word generation. We applied a modified Fast Correlation Based Filter (FCBF) ...
View more >OBJECTIVE: Brain-computer interface (BCI) research aims at developing communication devices for the motor disabled. Such devices are not driven by muscle activity, but by brain activity recorded during different mental tasks. We present here the comparison of phase synchronization and power spectral density (PSD) features, computed from broadband and narrowband filtered EEG signals and their ability to discriminate three mental tasks. METHODS: EEG signals were recorded from five subjects while performing left and right hand movement imagination and word generation. We applied a modified Fast Correlation Based Filter (FCBF) [9] for the purpose of feature selection. RESULTS: We found that the features were selected from electrode signals corresponding to neurophysiological evidence, i.e. electrodes lying over the motor cortex. PSD and phase locking value (PLV) features were more discriminative when computed from narrowband (8-12 Hz) and broadband (8-30 Hz) filtered signals respectively. CONCLUSIONS: The generalization performance is as good as the one obtained with SVM-rfe, but this algorithm is faster and selects fewer features. These properties may make FCBF a valuable tool for further improvement of BCIs.
View less >
View more >OBJECTIVE: Brain-computer interface (BCI) research aims at developing communication devices for the motor disabled. Such devices are not driven by muscle activity, but by brain activity recorded during different mental tasks. We present here the comparison of phase synchronization and power spectral density (PSD) features, computed from broadband and narrowband filtered EEG signals and their ability to discriminate three mental tasks. METHODS: EEG signals were recorded from five subjects while performing left and right hand movement imagination and word generation. We applied a modified Fast Correlation Based Filter (FCBF) [9] for the purpose of feature selection. RESULTS: We found that the features were selected from electrode signals corresponding to neurophysiological evidence, i.e. electrodes lying over the motor cortex. PSD and phase locking value (PLV) features were more discriminative when computed from narrowband (8-12 Hz) and broadband (8-30 Hz) filtered signals respectively. CONCLUSIONS: The generalization performance is as good as the one obtained with SVM-rfe, but this algorithm is faster and selects fewer features. These properties may make FCBF a valuable tool for further improvement of BCIs.
View less >
Journal Title
Methods of Information in Medicine
Volume
2
Publisher URI
Copyright Statement
© 2009 Schattauer. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal website for access to the definitive, published version
Subject
Biological Sciences
Information and Computing Sciences
Medical and Health Sciences