Enhancement of Classification Accuracy of a Time-frequency Approach for an EEG-based Brain-computer Interface

Summary

Objectives : The aim of this paper is to develop a new algorithm to enhance the performance of EEG-based brain-computer interface (BCI).

Methods : We improved our time-frequency approach of classification of motor imagery (MI) tasks for BCI applications. The approach consists of Laplacian filtering, band-pass filtering and classification by correlation of time-frequency-spatial patterns.

Results and Conclusions : Through off-line analysis of data collected during a “cursor control" experiment, we evaluated the capability of our new method to reveal major features of the EEG control for enhancement of MI classification accuracy. The pilot results in a human subject are promising, with an accuracy rate of 96.1%.

Keywords

Time-frequency analysis - EEG - brain-computer interface - motor imagery

• References

• 1. Wolpaw JR, McFarland DJ, Neat GW, Forneris CA. An EEG-based brain computer interface for cursor control. Electroencephalogr Clin Neurophysiol 1991; 78: 252-259.
• 2. Pfurtscheller G, Neuper C, Flotzinger D, Pregenzer M. EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol 1997; 103: 642-651.
• 3. Pfurtscheller G. Event-related desynchronization. Elsevier Publisher 1999; 303-325.
• 4. Nicolelis ML. Actions from thoughts. Nature 2001; 409: 403-407.
• 5. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G., Vaughan TM. Brain computer interfaces for communication and control. Clin Neurophysiol 2002; 113: 767-791.
• 6. Donoghue J. Connecting cortex to machines: recent advances in brain interfaces. Nature Neurosci Suppl 2002; 5: 1085-1088.
• 7. Sheikh H, McFarland DJ, Sarnacki WA, Wolpaw JR. Electroencephalographic (EEG)-based communication: EEG control versus system performance in humans. Neuroscience Letters 2003; 345: 89-92.
• 8. Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng 2004; 51 (06) 1034-1043.
• 9. Qin L, Ding L, He B. Motor imagery classification by means of source analysis for brain-computer interface applications. JNeural Eng 2004; 1: 135-141.
• 10. Wang T, He B. An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain-computer interface. J Neural Eng 2004; 1-7.
• 11. Wang T, Deng J, He B. Classifying EEG-based motor imagery tasks by means of time-frequency synthesized spatial patterns. Clin Neurophysiol 2004; 115: 2744-2753.
• 12. Vallabhaneni A, Wang T, He B. Brain Computer Interface. In: He B Neural Engineering. Kluwer Academic: Plenum Publishers; 2005
• 13. Yamawaki N, Wilke C, Liu ZM, He B. Enhancement of performance of an EEG-based brain-computer interface by means of a time-frequency approach. The 2nd Inter Conf On Neural Eng 2005; 94-96.