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

Wang Y, Veluvolu KC, Lee M - J Neuroeng Rehabil (2013)

Bottom Line: Time-Frequency analysis of electroencephalogram (EEG) during different mental tasks received significant attention.The proposed method is applied to BCI Competition data IV for ERD detection in comparison with existing methods.For ERD detection, BMFLC-KF outperforms STFT and BMFLC-KS in real-time applicability with low computational requirement.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of IT Engineering, Kyungpook National University, 1370 Sanyuk-dong, Daegu, 702-701, South Korea. veluvolu@ee.knu.ac.kr.

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.

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Related in: MedlinePlus

Estimation performance for synthetic signal S4(t).(a) Time-frequency mappings for BMFLC-KF, BMFLC-KS and BMFLC-LMS; (b) Estimated weights of BMFLC-LMS; (c) Estimated weights of BMFLC-KF; (d) Estimated weights of BMFLC-KS.
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Figure 8: Estimation performance for synthetic signal S4(t).(a) Time-frequency mappings for BMFLC-KF, BMFLC-KS and BMFLC-LMS; (b) Estimated weights of BMFLC-LMS; (c) Estimated weights of BMFLC-KF; (d) Estimated weights of BMFLC-KS.

Mentions: Although the amplitude accuracy of BMFLC-LMS is high, its corresponding frequency components cannot adjust to the sudden changes in the frequency characteristics of the signal. The weights of BMFLC-LMS requires longer duration to track the changes in the frequency characteristics of the signal. To highlight the problem, synthesized signal S4(t) is employed. Time-frequency maps for BMFLC-LMS, BMFLC-KF/KS are shown in Figure 8(a). Individual weights of BMFLC are shown in Figure 8(b)-(d). It can be clearly seen in Figure 8(d) that the frequency weights of BMFLC-LMS require more time to settle to steady-state. Whereas BMFLC-KF/KS weights settle to correct frequency values immediately as shown in Figure 8(c)-(d). When the frequency components in signal S4 change at 60s, the previous settled frequency weights slowly decreases to 0 as the new frequency components gradually increase. BMFLC-KF/KS can track the sudden changes in frequency, whereas the corresponding weights in BMFLC-LMS does not settle. This clearly highlight the inadequacy of the BMFLC-LMS for extracting fast changing frequency characteristics in the signal.


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

Wang Y, Veluvolu KC, Lee M - J Neuroeng Rehabil (2013)

Estimation performance for synthetic signal S4(t).(a) Time-frequency mappings for BMFLC-KF, BMFLC-KS and BMFLC-LMS; (b) Estimated weights of BMFLC-LMS; (c) Estimated weights of BMFLC-KF; (d) Estimated weights of BMFLC-KS.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4222486&req=5

Figure 8: Estimation performance for synthetic signal S4(t).(a) Time-frequency mappings for BMFLC-KF, BMFLC-KS and BMFLC-LMS; (b) Estimated weights of BMFLC-LMS; (c) Estimated weights of BMFLC-KF; (d) Estimated weights of BMFLC-KS.
Mentions: Although the amplitude accuracy of BMFLC-LMS is high, its corresponding frequency components cannot adjust to the sudden changes in the frequency characteristics of the signal. The weights of BMFLC-LMS requires longer duration to track the changes in the frequency characteristics of the signal. To highlight the problem, synthesized signal S4(t) is employed. Time-frequency maps for BMFLC-LMS, BMFLC-KF/KS are shown in Figure 8(a). Individual weights of BMFLC are shown in Figure 8(b)-(d). It can be clearly seen in Figure 8(d) that the frequency weights of BMFLC-LMS require more time to settle to steady-state. Whereas BMFLC-KF/KS weights settle to correct frequency values immediately as shown in Figure 8(c)-(d). When the frequency components in signal S4 change at 60s, the previous settled frequency weights slowly decreases to 0 as the new frequency components gradually increase. BMFLC-KF/KS can track the sudden changes in frequency, whereas the corresponding weights in BMFLC-LMS does not settle. This clearly highlight the inadequacy of the BMFLC-LMS for extracting fast changing frequency characteristics in the signal.

Bottom Line: Time-Frequency analysis of electroencephalogram (EEG) during different mental tasks received significant attention.The proposed method is applied to BCI Competition data IV for ERD detection in comparison with existing methods.For ERD detection, BMFLC-KF outperforms STFT and BMFLC-KS in real-time applicability with low computational requirement.

View Article: PubMed Central - HTML - PubMed

Affiliation: College of IT Engineering, Kyungpook National University, 1370 Sanyuk-dong, Daegu, 702-701, South Korea. veluvolu@ee.knu.ac.kr.

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.

Show MeSH
Related in: MedlinePlus