The EEG De-noising Research Based on Wavelet and Hilbert Transform Method

Yuan, Fei-long; Luo, Zhizeng

doi:10.1109/iccsee.2012.420

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Then, the baseline of EEG signals was reset called baseline drift [36]. There are several methods to remove baseline drift, such as the median filtering method [37], wavelet transform method [38], high-pass filtering method [39, 40], and curve fitting method [41]. In this paper, weighted least squares- (WLS-) [42] based local linear regression method is employed to fit the original data of each segment to zero.…”

Section: Methodsmentioning

confidence: 99%

G-Causality Brain Connectivity Differences of Finger Movements between Motor Execution and Motor Imagery

Chen

Zhang

Belkacem

et al. 2019

Journal of Healthcare Engineering

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Motor imagery is one of the classical paradigms which have been used in brain-computer interface and motor function recovery. Finger movement-based motor execution is a complex biomechanical architecture and a crucial task for establishing most complicated and natural activities in daily life. Some patients may suffer from alternating hemiplegia after brain stroke and lose their ability of motor execution. Fortunately, the ability of motor imagery might be preserved independently and worked as a backdoor for motor function recovery. The efficacy of motor imagery for achieving significant recovery for the motor cortex after brain stroke is still an open question. In this study, we designed a new paradigm to investigate the neural mechanism of thirty finger movements in two scenarios: motor execution and motor imagery. Eleven healthy participants performed or imagined thirty hand gestures twice based on left and right finger movements. The electroencephalogram (EEG) signal for each subject during sixty trials left and right finger motor execution and imagery were recorded during our proposed experimental paradigm. The Granger causality (G-causality) analysis method was employed to analyze the brain connectivity and its strength between contralateral premotor, motor, and sensorimotor areas. Highest numbers for G-causality trials of 37 ± 7.3, 35.5 ± 8.8, 36.3 ± 10.3, and 39.2 ± 9.0 and lowest Granger causality coefficients of 9.1 ± 3.2, 10.9 ± 3.7, 13.2 ± 0.6, and 13.4 ± 0.6 were achieved from the premotor to motor area during execution/imagination tasks of right and left finger movements, respectively. These results provided a new insight into motor execution and motor imagery based on hand gestures, which might be useful to build a new biomarker of finger motor recovery for partially or even completely plegic patients. Furthermore, a significant difference of the G-causality trial number was observed during left finger execution/imagery and right finger imagery, but it was not observed during the right finger execution phase. Significant difference of the G-causality coefficient was observed during left finger execution and imagery, but it was not observed during right finger execution and imagery phases. These results suggested that different MI-based brain motor function recovery strategies should be taken for right-hand and left-hand patients after brain stroke.

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Section: Methodsmentioning

confidence: 99%

G-Causality Brain Connectivity Differences of Finger Movements between Motor Execution and Motor Imagery

Chen

Zhang

Belkacem

et al. 2019

Journal of Healthcare Engineering

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“…However, these are oftentimes inexact replications, as parameters like filter width alter the output in subtle but meaningful ways. The pervasiveness of the Fourier transform across techniques offers an excellent way to double-check one’s data processing for hidden bugs by comparing outputs derived via wavelet vs. filter-Hilbert techniques (Le Van Quyen et al, 2001 ; Yuan and Luo, 2012 ).…”

Section: Overview Of Extracting Features From Eegmentioning

confidence: 99%

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

Bridwell

Cavanagh

Collins

et al. 2018

Front. Hum. Neurosci.

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Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.

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EEG Denoising Based on Empirical Mode Decomposition and Mutual Information

Mert

Akan

2014

IFMBE Proceedings

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The EEG De-noising Research Based on Wavelet and Hilbert Transform Method

Cited by 8 publications

References 5 publications

G-Causality Brain Connectivity Differences of Finger Movements between Motor Execution and Motor Imagery

G-Causality Brain Connectivity Differences of Finger Movements between Motor Execution and Motor Imagery

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

EEG Denoising Based on Empirical Mode Decomposition and Mutual Information

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