A Streamlined Phase-Based Approach for Distinguishing EEG Motor Imagery Tasks

Dr. Chen Liwei; Dr. Julian Thompson

Authors

Dr. Chen Liwei Department of Brain and Cognitive Sciences, Tsinghua University, China
Dr. Julian Thompson School of Engineering, University of Glasgow, United Kingdom

Keywords:

EEG, motor imagery, brain–computer interface, phase-based features, signal processing, classification, feature extraction, neural decoding, computational neuroscience, real-time BCI systems

Abstract

Accurate classification of electroencephalogram (EEG) motor imagery tasks is critical for advancing brain–computer interface (BCI) applications. This paper proposes a streamlined phase-based approach to distinguish motor imagery tasks by extracting and leveraging phase information inherent in EEG signals. The method involves decomposing EEG data into relevant frequency bands, computing phase features using analytic signal techniques, and applying feature selection to enhance discriminative power. Experimental evaluation on benchmark motor imagery datasets demonstrates that the phase-based features significantly improve classification accuracy compared to traditional amplitude-based methods. The approach is computationally efficient, robust to noise, and adaptable to real-time BCI systems. These findings underscore the potential of phase information as a valuable modality for refining motor imagery recognition and optimizing user performance in neurotechnology applications.

References

A.M., Lazăr, L. Davlea, R. Ursulean, A. Maiorescu, B. Teodorescu

Interfața creier-calculator - Paradigme posibile. CERMI, 2009. (in Romanian)

G. Pfurtscheller, C. Neuper

“Motor Imagery and Direct Brain-Computer Communication.” Proceedings IEEE, Vol. 89, No. 7, pp. 1123–1134, 2001.

A. Bashashati, M. Fatourechi, K.R. Ward, E.G. Birch

“A Survey of Signal Processing Algorithms in Brain–Computer Interfaces Based on Electrical Brain Signals.” Journal of Neural Engineering, Vol. 4, pp. 32–57, 2007.

E. Gysels, P. Celka

“Phase Synchronization for the Recognition of Mental Tasks in a Brain–Computer Interface.” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 12, No. 4, December 2004.

Le Song, E. Gordon, E. Gysels

“Phase Synchrony Rate for the Recognition of Motor Imagery in Brain-Computer Interface.” Advances in Neural Information Processing Systems, Vol. 18, pp. 1265–1272, 2006.

D. Krusienski, D. McFarland, J. Wolpaw

“Value of Amplitude, Phase, and Coherence Features for a Sensorimotor Rhythm-Based Brain–Computer Interface.” Brain Research Bulletin, Vol. 87(1), pp. 130–134, January 4, 2012.

M. Le Van Quyen et al.

“Comparison of Hilbert Transform and Wavelet Methods for the Analysis of Neuronal Synchrony.” Journal of Neuroscience Methods, Vol. 111, pp. 83–98, 2001.

J.P. Lachaux, E. Rodriguez, J. Martinerie, F.J. Varela

“Measuring Phase Synchrony in Brain Signals.” Human Brain Mapping, Vol. 8, pp. 194–208, 1999.

Yijun Wang, Bo Hong, Xiaorong Gao, Shangkai Gao

“Phase Synchrony Measurement in Motor Cortex for Classifying Single-Trial EEG During Motor Imagery.” Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA, August 30–September 3, 2006.

G. Schalk, D.J. McFarland, T. Hinterberger, N. Birbaumer, J.R. Wolpaw

“BCI2000: A General-Purpose Brain–Computer Interface (BCI) System.” IEEE Transactions on Biomedical Engineering, Vol. 51(6), pp. 1034–1043, 2004.

A.L. Goldberger et al.

“PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals.” Circulation, Vol. 101(23): e215–e220, June 13, 2000.

(Available online: http://circ.ahajournals.org/cgi/content/full/101/23/e215)

J. Sleight, P. Pillai, S. Mohan

“Classification of Executed and Imagined Motor Movement EEG Signals.” Ann Arbor: University of Michigan, pp. 1–10, 2009. (Retrieved f.)

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