Online detection of amplitude modulation of motor-related EEG desynchronization using a lock-in amplifier: Comparison with a fast Fourier transform, a continuous wavelet transform, and an autoregressive algorithm

Kenji Kato, Kensho Takahashi, Nobuaki Mizuguchi, Junichi Ushiba

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Background Neurofeedback of event-related desynchronization (ERD) in electroencephalograms (EEG) of the sensorimotor cortex (SM1) using a brain–computer interface (BCI) paradigm is a powerful tool to promote motor recovery from post-stroke hemiplegia. However, the feedback delay attenuates the degree of motor learning and neural plasticity. New method The present study aimed to shorten the delay time to estimate amplitude modulation of the motor-imagery-related alpha and beta SM1-ERD using a lock-in amplifier (LIA) algorithm. The delay time was evaluated by calculating the value of the maximal correlation coefficient (MCC) between the time-series trace of ERDs extracted by the online LIA algorithm and those identified by an offline algorithm with the Hilbert transform (HT). Results The MCC and delay values used to estimate the ERDs calculated by the LIA were 0.89 ± 0.032 and 200 ± 9.49 ms, respectively. Comparison with Existing Method(s) The delay time and MCC values were significantly improved compared with those calculated by the conventional fast Fourier transformation (FFT), continuous Wavelet transformation (CWT), and autoregressive (AR) algorithms. Moreover, the coefficients of variance of the delay time and MCC values across trials were significantly lower in the LIA compared with the FFT, CWT, and AR algorithms. Conclusions These results indicate that the LIA improved the detection delay, accuracy, and stability for estimating amplitude modulation of motor-related SM1-ERD. This would be beneficial for BCI paradigms to facilitate neurorehabilitation in patients with motor deficits.

Original languageEnglish
Pages (from-to)289-298
Number of pages10
JournalJournal of Neuroscience Methods
Volume293
DOIs
Publication statusPublished - 2018 Jan 1

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Wavelet Analysis
Fourier Analysis
Electroencephalography
Neurofeedback
Neuronal Plasticity
Hemiplegia
Imagery (Psychotherapy)
Stroke
Learning

Keywords

  • Brain–computer interface (BCI)
  • Electroencephalogram (EEG)
  • Event-related desynchronization (ERD)
  • Lock-in amplifier (LIA)
  • Motor imagery
  • Online neurofeedback
  • Sensorimotor cortex (SM1)

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

@article{c02c125d5c084641be2fc127514e6005,
title = "Online detection of amplitude modulation of motor-related EEG desynchronization using a lock-in amplifier: Comparison with a fast Fourier transform, a continuous wavelet transform, and an autoregressive algorithm",
abstract = "Background Neurofeedback of event-related desynchronization (ERD) in electroencephalograms (EEG) of the sensorimotor cortex (SM1) using a brain–computer interface (BCI) paradigm is a powerful tool to promote motor recovery from post-stroke hemiplegia. However, the feedback delay attenuates the degree of motor learning and neural plasticity. New method The present study aimed to shorten the delay time to estimate amplitude modulation of the motor-imagery-related alpha and beta SM1-ERD using a lock-in amplifier (LIA) algorithm. The delay time was evaluated by calculating the value of the maximal correlation coefficient (MCC) between the time-series trace of ERDs extracted by the online LIA algorithm and those identified by an offline algorithm with the Hilbert transform (HT). Results The MCC and delay values used to estimate the ERDs calculated by the LIA were 0.89 ± 0.032 and 200 ± 9.49 ms, respectively. Comparison with Existing Method(s) The delay time and MCC values were significantly improved compared with those calculated by the conventional fast Fourier transformation (FFT), continuous Wavelet transformation (CWT), and autoregressive (AR) algorithms. Moreover, the coefficients of variance of the delay time and MCC values across trials were significantly lower in the LIA compared with the FFT, CWT, and AR algorithms. Conclusions These results indicate that the LIA improved the detection delay, accuracy, and stability for estimating amplitude modulation of motor-related SM1-ERD. This would be beneficial for BCI paradigms to facilitate neurorehabilitation in patients with motor deficits.",
keywords = "Brain–computer interface (BCI), Electroencephalogram (EEG), Event-related desynchronization (ERD), Lock-in amplifier (LIA), Motor imagery, Online neurofeedback, Sensorimotor cortex (SM1)",
author = "Kenji Kato and Kensho Takahashi and Nobuaki Mizuguchi and Junichi Ushiba",
year = "2018",
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T2 - Comparison with a fast Fourier transform, a continuous wavelet transform, and an autoregressive algorithm

AU - Kato, Kenji

AU - Takahashi, Kensho

AU - Mizuguchi, Nobuaki

AU - Ushiba, Junichi

PY - 2018/1/1

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N2 - Background Neurofeedback of event-related desynchronization (ERD) in electroencephalograms (EEG) of the sensorimotor cortex (SM1) using a brain–computer interface (BCI) paradigm is a powerful tool to promote motor recovery from post-stroke hemiplegia. However, the feedback delay attenuates the degree of motor learning and neural plasticity. New method The present study aimed to shorten the delay time to estimate amplitude modulation of the motor-imagery-related alpha and beta SM1-ERD using a lock-in amplifier (LIA) algorithm. The delay time was evaluated by calculating the value of the maximal correlation coefficient (MCC) between the time-series trace of ERDs extracted by the online LIA algorithm and those identified by an offline algorithm with the Hilbert transform (HT). Results The MCC and delay values used to estimate the ERDs calculated by the LIA were 0.89 ± 0.032 and 200 ± 9.49 ms, respectively. Comparison with Existing Method(s) The delay time and MCC values were significantly improved compared with those calculated by the conventional fast Fourier transformation (FFT), continuous Wavelet transformation (CWT), and autoregressive (AR) algorithms. Moreover, the coefficients of variance of the delay time and MCC values across trials were significantly lower in the LIA compared with the FFT, CWT, and AR algorithms. Conclusions These results indicate that the LIA improved the detection delay, accuracy, and stability for estimating amplitude modulation of motor-related SM1-ERD. This would be beneficial for BCI paradigms to facilitate neurorehabilitation in patients with motor deficits.

AB - Background Neurofeedback of event-related desynchronization (ERD) in electroencephalograms (EEG) of the sensorimotor cortex (SM1) using a brain–computer interface (BCI) paradigm is a powerful tool to promote motor recovery from post-stroke hemiplegia. However, the feedback delay attenuates the degree of motor learning and neural plasticity. New method The present study aimed to shorten the delay time to estimate amplitude modulation of the motor-imagery-related alpha and beta SM1-ERD using a lock-in amplifier (LIA) algorithm. The delay time was evaluated by calculating the value of the maximal correlation coefficient (MCC) between the time-series trace of ERDs extracted by the online LIA algorithm and those identified by an offline algorithm with the Hilbert transform (HT). Results The MCC and delay values used to estimate the ERDs calculated by the LIA were 0.89 ± 0.032 and 200 ± 9.49 ms, respectively. Comparison with Existing Method(s) The delay time and MCC values were significantly improved compared with those calculated by the conventional fast Fourier transformation (FFT), continuous Wavelet transformation (CWT), and autoregressive (AR) algorithms. Moreover, the coefficients of variance of the delay time and MCC values across trials were significantly lower in the LIA compared with the FFT, CWT, and AR algorithms. Conclusions These results indicate that the LIA improved the detection delay, accuracy, and stability for estimating amplitude modulation of motor-related SM1-ERD. This would be beneficial for BCI paradigms to facilitate neurorehabilitation in patients with motor deficits.

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KW - Online neurofeedback

KW - Sensorimotor cortex (SM1)

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