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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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Time-frequency mappings of various methods for synthetic signal S3(t).(a) Synthetic signal S3(t); (b) Time-frequency mapping of BMFLC-LMS with frequency spacing Δf=0.2 Hz; (c) Time-frequency mapping of BMFLC-KF with frequency spacing Δf=0.2 Hz; (d) Time-frequency mapping of BMFLC-KS with frequency spacing Δf=0.2 Hz; (e) Time-frequency mapping of STFT; (f) Time-frequency mapping of CWT; (g) Time-frequency mapping of BMFLC-KS with frequency spacing Δf=0.1 Hz; (h) Time-frequency mapping of BMFLC-KS with frequency spacing Δf=1 Hz.
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Figure 6: Time-frequency mappings of various methods for synthetic signal S3(t).(a) Synthetic signal S3(t); (b) Time-frequency mapping of BMFLC-LMS with frequency spacing Δf=0.2 Hz; (c) Time-frequency mapping of BMFLC-KF with frequency spacing Δf=0.2 Hz; (d) Time-frequency mapping of BMFLC-KS with frequency spacing Δf=0.2 Hz; (e) Time-frequency mapping of STFT; (f) Time-frequency mapping of CWT; (g) Time-frequency mapping of BMFLC-KS with frequency spacing Δf=0.1 Hz; (h) Time-frequency mapping of BMFLC-KS with frequency spacing Δf=1 Hz.

Mentions: Another factor that affects the spectral estimation is the selection of frequency gap Δf. To study the sensitivity of the method, synthesized signal S3(t) is employed and the results for various Δf are shown in Figure 6. When source signal has several frequency components located closely in the spectral domain, STFT provides better spectral estimation compared to CWT. It is also clear that, with STFT the estimated amplitude is distorted. A frequency gap of 0.2 Hz is employed for the analysis with BMFLC based methods. For a frequency gap Δf=0.1 Hz and 0.2 Hz, spectral estimation obtained with BMFLC-KF/KS is better compared to STFT. With BMFLC-KS the initial adaptation period is reduced and the improved performance can be seen in Figure 6(d). As the source signal S3(t) has closely spaced frequencies, the results for BMFLC with frequency gap 1 Hz are not accurate as shown in Figure 6(h). Furthermore, the number of frequency weights in BMFLC based methods can affect the amplitude estimation. As shown in Figure 6(g), when Δf=0.1 Hz gap is used, the estimated amplitude is smaller compared to the actual amplitude of the synthesized signal S3(t). These results clearly shows that the an appropriate frequency gap should be selected for accurate spectral estimation.


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)

Time-frequency mappings of various methods for synthetic signal S3(t).(a) Synthetic signal S3(t); (b) Time-frequency mapping of BMFLC-LMS with frequency spacing Δf=0.2 Hz; (c) Time-frequency mapping of BMFLC-KF with frequency spacing Δf=0.2 Hz; (d) Time-frequency mapping of BMFLC-KS with frequency spacing Δf=0.2 Hz; (e) Time-frequency mapping of STFT; (f) Time-frequency mapping of CWT; (g) Time-frequency mapping of BMFLC-KS with frequency spacing Δf=0.1 Hz; (h) Time-frequency mapping of BMFLC-KS with frequency spacing Δf=1 Hz.
© Copyright Policy - open-access
Related In: Results  -  Collection

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Figure 6: Time-frequency mappings of various methods for synthetic signal S3(t).(a) Synthetic signal S3(t); (b) Time-frequency mapping of BMFLC-LMS with frequency spacing Δf=0.2 Hz; (c) Time-frequency mapping of BMFLC-KF with frequency spacing Δf=0.2 Hz; (d) Time-frequency mapping of BMFLC-KS with frequency spacing Δf=0.2 Hz; (e) Time-frequency mapping of STFT; (f) Time-frequency mapping of CWT; (g) Time-frequency mapping of BMFLC-KS with frequency spacing Δf=0.1 Hz; (h) Time-frequency mapping of BMFLC-KS with frequency spacing Δf=1 Hz.
Mentions: Another factor that affects the spectral estimation is the selection of frequency gap Δf. To study the sensitivity of the method, synthesized signal S3(t) is employed and the results for various Δf are shown in Figure 6. When source signal has several frequency components located closely in the spectral domain, STFT provides better spectral estimation compared to CWT. It is also clear that, with STFT the estimated amplitude is distorted. A frequency gap of 0.2 Hz is employed for the analysis with BMFLC based methods. For a frequency gap Δf=0.1 Hz and 0.2 Hz, spectral estimation obtained with BMFLC-KF/KS is better compared to STFT. With BMFLC-KS the initial adaptation period is reduced and the improved performance can be seen in Figure 6(d). As the source signal S3(t) has closely spaced frequencies, the results for BMFLC with frequency gap 1 Hz are not accurate as shown in Figure 6(h). Furthermore, the number of frequency weights in BMFLC based methods can affect the amplitude estimation. As shown in Figure 6(g), when Δf=0.1 Hz gap is used, the estimated amplitude is smaller compared to the actual amplitude of the synthesized signal S3(t). These results clearly shows that the an appropriate frequency gap should be selected for accurate spectral estimation.

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