Top

Journal of NeuroEngineering and Rehabilitation

Published in:

Open Access 01-12-2013 | Research

Time-frequency analysis of band-limited EEG with BMFLC and Kalman filter for BCI applications

Authors: Yubo Wang, Kalyana C Veluvolu, Minho Lee

Published in: Journal of NeuroEngineering and Rehabilitation | Issue 1/2013

Abstract

Background

Time-Frequency analysis of electroencephalogram (EEG) during different mental tasks received significant attention. As EEG is non-stationary, time-frequency analysis is essential to analyze brain states during different mental tasks. Further, the time-frequency information of EEG signal can be used as a feature for classification in brain-computer interface (BCI) applications.

Methods

To accurately model the EEG, band-limited multiple Fourier linear combiner (BMFLC), a linear combination of truncated multiple Fourier series models is employed. A state-space model for BMFLC in combination with Kalman filter/smoother is developed to obtain accurate adaptive estimation. By virtue of construction, BMFLC with Kalman filter/smoother provides accurate time-frequency decomposition of the bandlimited signal.

Results

The proposed method is computationally fast and is suitable for real-time BCI applications. To evaluate the proposed algorithm, a comparison with short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for both synthesized and real EEG data is performed in this paper. The proposed method is applied to BCI Competition data IV for ERD detection in comparison with existing methods.

Conclusions

Results show that the proposed algorithm can provide optimal time-frequency resolution as compared to STFT and CWT. For ERD detection, BMFLC-KF outperforms STFT and BMFLC-KS in real-time applicability with low computational requirement.

Available only for authorised users

Buzsaki G, Draguhn A: Neuronal oscillations in cortical networks. Science 2004, 304: 1926-1929. 10.1126/science.1099745CrossRefPubMed

Olufsen MS, Whittington MA, Camperi M, Kopell N: New roles for the gamma rhythm: population tuning and preprocessing for the beta rhythm. J Comput Neurosci 2003, 14: 33-54. 10.1023/A:1021124317706CrossRefPubMed

Dimitriadis SI, Laskarisb NA, Tsirkac V, Vourkasd M, Micheloyannisc S: What does delta band tell us about cognitive processes: A mental calculation study. Neurosci Lett 2010, 483: 11-15. 10.1016/j.neulet.2010.07.034CrossRefPubMed

Pfurtscheller G, Muller-Putz GR, Scherer R, Neuper C: Rehabilitation with brain-computer interface systems. Computer 2008, 41: 1-8.CrossRef

Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughana TM: Brain computer interfaces for communication and control. Clin Neurophysiol 2002, 113: 767-791. 10.1016/S1388-2457(02)00057-3CrossRefPubMed

Blankertz B, Losch F, Krauledat M, Dornhege G, Curio G, Muller KR: The Berlin brain-computer interface: accurate performance from first-session in BCI-Naive subjects. IEEE Trans Biomed Eng 2008, 55: 2452-2462.CrossRefPubMed

Ferreira A, Celeste WC, Cheein FA, Bastos-Filho TF, Sarcinelli-Filho M, Carelli R: Human-machine interfaces based on EMG and EEG applied to robotic systems. J Neuroeng Rehabil 2008, 5: 10. 10.1186/1743-0003-5-10PubMedCentralCrossRefPubMed

Liu G, Zhang D, Meng J, Huang G, Zhu X: Unsupervised adaptation of electroencephalogram signal processing based on fuzzy C-means algorithm. Int J Adaptive Control Signal Process 2012, 26: 482-495. 10.1002/acs.1293CrossRef

Pfurtscheller G, Silva FLD: Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 1999, 110: 1842-1857. 10.1016/S1388-2457(99)00141-8CrossRefPubMed

10.

Prasad G, Herman P, Coyle D, McDonough S, Crosbie J: Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study. J Neuroeng Rehabil 2010, 7: 60. 10.1186/1743-0003-7-60PubMedCentralCrossRefPubMed

11.

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. 10.1016/S0013-4694(97)00080-1CrossRefPubMed

12.

Qin L, He B: A wavelet-based time-frequency analysis approach for classification of motor imagery for brain-computer interface applications. J Neural Eng 2005,2(4):65. 10.1088/1741-2560/2/4/001PubMedCentralCrossRefPubMed

13.

Yuan H, Doud A, Gururajan A, He B: Cortical imaging of event-related (de)synchronization during online control of brain-computer interface using minimum-norm estimates in frequency domain. IEEE Trans Neural Syst Rehabil Eng 2008, 16: 425-431.PubMedCentralCrossRefPubMed

14.

Herman P, Prasad G, McGinnity T, Coyle D: Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification. IEEE Trans Neural Syst Rehabil Eng 2008,16(4):317-326.CrossRefPubMed

15.

Krusienski DJ, McFarland DJ, Wolpaw JR: Value of amplitude, phase, and coherence features for a sensorimotor rhythm-based brain computer interface. Brain Res Bull 2012, 87: 130-134. 10.1016/j.brainresbull.2011.09.019PubMedCentralCrossRefPubMed

16.

McFarland DJ, Sarnacki WA, Wolpaw JR: Electroencephalographic (EEG) control of three-dimensional movement. J Neural Eng 2010,7(3):036007. 10.1088/1741-2560/7/3/036007PubMedCentralCrossRefPubMed

17.

Do AH, Wang PT, King CE, Abiri A, Nenadic Z: Brain-computer interface controlled functional electrical stimulation system for ankle movement. J Neuroeng Rehabil 2011, 8: 49. 10.1186/1743-0003-8-49PubMedCentralCrossRefPubMed

18.

Veluvolu KC, Wang Y, Kavuri SS: Adaptive estimation of EEG-rhythms for optimal band identification in BCI. J Neurosci Methods 2012, 203: 163-172. 10.1016/j.jneumeth.2011.08.035CrossRefPubMed

19.

McFarland DJ, Wolpaw JR: Sensorimotor rhythm-based brain computer interface (BCI): model order selection for autoregressive spectral analysis. J Neural Eng 2008, 5: 155-162. 10.1088/1741-2560/5/2/006PubMedCentralCrossRefPubMed

20.

Pfurtscheller G, Neuper C, Schlogl A, Lugger K: Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans Neural Syst Rehabil Eng 1998,6(NO.3):316-325.CrossRef

21.

Kiymik MK, Guler I, Dizibuyuk A, Akin M: Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Comput Biol Med 2005, 35: 603-616. 10.1016/j.compbiomed.2004.05.001CrossRefPubMed

22.

Allen DP, MacKinnon CD: Time frequency analysis of movement-related spectral power in EEG during repetitive movements: A comparison of methods. J Neurosci Methods 2010, 186: 107-115. 10.1016/j.jneumeth.2009.10.022PubMedCentralCrossRefPubMed

23.

Veluvolu KC, Ang WT: Estimation and filtering of physiological tremor for real-time compensation in surgical robotics applications. Int J Med Robot Comput Assist Surg 2010, 6: 334-342. 10.1002/rcs.340CrossRef

24.

Latt WT, Veluvolu KC, Ang WT: Drift-free position estimation of periodic or quasi-periodic motion using inertial sensors. Sensors 2011,11(6):5931-5951.PubMedCentralCrossRefPubMed

25.

Gabor D: Theory of communication. Electrical Eng - Part III: Radio Commun Eng J Inst 1946, 93: 442-445.

26.

Mallat S: a Wavelet Tour of Signal Processing. Burlington: Elsevier; 2009.

27.

Daubechies I: The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inf Theory 1990,36(5):961-1005. 10.1109/18.57199CrossRef

28.

Zhan Y, Halliday D, Jiang P, Liu X, Feng J: Detecting time-dependent coherence between non-stationary electrophysiological signals- A combined statistical and time-frequency approach. J Neurosci Methods 2006, 156: 322-332. 10.1016/j.jneumeth.2006.02.013CrossRefPubMed

29.

Goupillaud P, Grossmann A, Morlet J: Cycle-octave and related transforms in seismic signal analysis. Geoexploration 1984, 23: 85-102. 10.1016/0016-7142(84)90025-5CrossRef

30.

Aguiar-Conraia L, Soares MJ: The continuous wavelet transform: a primer. NIPE Working Paper Series 2011, NIPE WP 16: 1-46.

31.

Vaz CA, Thakor NV: Adaptive Fourier estimation of time-varying evoked potentials. IEEE Trans Biomed Eng 1989,36(4):448-455. 10.1109/10.18751CrossRefPubMed

32.

Wang Y, Veluvolu KC, Cho JH, Defoort M: Adaptive estimation of EEG for subject-specific reactive band identification and improved ERD detection. Neurosci Lett 2012,528(2):137-42. 10.1016/j.neulet.2012.09.001CrossRefPubMed

33.

Haykin S: Adaptive Filter Theory. Upper Saddle River: Prentice Hall; 2002.

34.

Tarvainen MP, Hiltunen JK, Ranta-aho PO, Karjalainen PA: Estimation of nonstationary EEG with Kalman smoother approach: an application to event-related synchronization (ERS). IEEE Trans Biomed Eng 2004,51(3):516-524. 10.1109/TBME.2003.821029CrossRefPubMed

35.

Shumway RH, Stoffer DS: Time Series Analysis and Its Applications. New York: Springer; 2000.CrossRef

36.

Simon D: Optimal State Estimation. New Jersey: Wiley; 2006.CrossRef

37.

Guo L: Estimating time-varying parameters by the Kalman filter based algorithm: stability and convergence. IEEE Trans Automatic Control 1990,35(2):141-147. 10.1109/9.45169CrossRef

38.

Guo L, Ljung L: Exponential stability of general tracking algorithms. IEEE Trans Automatic Control 1995,40(8):1376-1387. 10.1109/9.402229CrossRef

39.

Ljung L, Gunnarsson S: Adaptation and tracking in system identification: a survey. Automatica 1990, 26: 7-21. 10.1016/0005-1098(90)90154-ACrossRef

40.

Cao L, Schwartz HM: Exponential convergence of the Kalman filter based parameter estimation algorithm. Int J Adaptive Control Signal Process 2003,17(10):763-783. 10.1002/acs.774CrossRef

41.

Brunner C, Naeem M, Leeb R, Graimann B, Pfurtscheller G: Spatial filtering and selection of optimized components in four class motor imagery data using independent components analysis. Pattern Recognit Lett 2007, 28: 957-964. 10.1016/j.patrec.2007.01.002CrossRef

42.

Graimanna B, Huggins J, Levineb S, Pfurtscheller G: Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data. Clin Neurophysiol 2002, 113: 43-47. 10.1016/S1388-2457(01)00697-6CrossRef

43.

Chang DC, Wu WR: Maneuvering target tracking with high-order correlated noise - a multirate Kalman filtering approach. Wireless Pers Commun 2001, 17: 103-123. 10.1023/A:1008965502460CrossRef

44.

Rockmore D: The FFT: an algorithm the whole family can use. Comput Sci Eng 2000, 2: 60-64. 10.1109/5992.814659CrossRef

45.

Do AH, Wang PT, King CE, Abiri A, Nenadic Z: Brain computer interface controlled functional electrical stimulation system for ankle movement. J NeuroEng Rehabil 2011, 8: 1-21. 10.1186/1743-0003-8-1CrossRef

Title: Time-frequency analysis of band-limited EEG with BMFLC and Kalman filter for BCI applications
Authors: Yubo Wang
Kalyana C Veluvolu
Minho Lee
Publication date: 01-12-2013
Publisher: BioMed Central
Published in: Journal of NeuroEngineering and Rehabilitation / Issue 1/2013
Electronic ISSN: 1743-0003
DOI: https://doi.org/10.1186/1743-0003-10-109

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Time-frequency analysis of band-limited EEG with BMFLC and Kalman filter for BCI applications

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2013

Asymmetrical slip propensity: required coefficient of friction

Development of a multi-electrode array for spinal cord epidural stimulation to facilitate stepping and standing after a complete spinal cord injury in adult rats

Effects of robotic guidance on the coordination of locomotion

Leg surface electromyography patterns in children with neuro-orthopedic disorders walking on a treadmill unassisted and assisted by a robot with and without encouragement

Does use of a virtual environment change reaching while standing in patients with traumatic brain injury?

Simulation of normal and pathological gaits using a fusion knowledge strategy