Fast Feature Selection to Compare Broadband with Narrowband Phase Synchronization in Brain-computer Interfaces

Summary

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.

Keywords

Brain-computer interface - broadband phase synchronization - EEG - Fast Correlation Based Filter

• References

• 1. Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM. Brain-Computer Interface technology: A review of the first International meeting. IEEE Trans Rehab Eng 2000; 8 (02) 164-173.
• 2. Vaughan T, Heetderks W, Trejo L, Rymer W, Weinrich M, Moore M, Kübler A, Dobkin B, Birbaumer N, Donchin E, Wolpaw E, Wolpaw J. Brain-Computer Interface technology: A review of the second International meeting. IEEE Trans Neur Syst Rehab Eng 2003; 11 (02) 94-109.
• 3. Wickelgren I. Tapping the mind. Science 2003; 299: 496-499.
• 4. Gysels E, Celka P. Phase synchronization for the recognition of mental tasks in a Brain Computer Interface. IEEE Trans Neur Syst Rehab Eng 2004; 12 (04) 406-415.
• 5. Gysels E, Renevey P, Celka P. SVM-based recursive feature elimination to compare phase synchronization computed from broadband and narrowband EEG signals in Brain-Computer Interfaces. Signal Processing, Special issue on neuronal coordination in the brain: a signal processing perspective 2005 85. 2178-2189.
• 6. Pfurtscheller G, Neuper C. Motor Imagery and direct brain-computer communication. Proc of IEEE 2001; 89 (07) 1123-1134.
• 7. Lachaux JP, Rodriguez E, Martinerie J, Varela FJ. Measuring phase synchrony in brain signals. Hum Br Map 1999; 8: 194-208
• 8. Gysels E, Celka E. Selection of synchronization and power features in Brain Machine Interfaces. Proc 2nd Int BCI Wokshop and Training Course 2004, Biomedizinische Technik. 2004 49 01 51-52.
• 9. Yu L, Liu H. Efficient Feature Selection via Analysis of Relevance and Redundancy. J Machine Learning Research 2004; 5: 1205-1224.