Motion Artifacts Correction From EEG and fNIRS Signals Using Novel Multiresolution Analysis

Abstract

Physiological signal measurement and processing is increasingly becoming popular in the ambulatory setting as the hospital-centric treatment is moving towards wearable and ubiquitous monitoring. Most of the physiological signals are highly susceptible to various types of noises especially movement artifacts. The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals are no exception to this motion artifacts, which becomes particularly prominent in the ambulatory setting. Since successful detection of various neurological disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove motion artifacts from these two signal modalities using reliable and robust methods. This paper proposes three multiresolution analysis techniques: i) Variational mode decomposition (VMD), ii) VMD in combination with principal component analysis (VMD-PCA), and iii) VMD in combination with canonical correlation analysis (VMD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these novel techniques is validated using the difference in the signal to noise ratio (𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥) and percentage reduction in motion artifacts (𝜂𝜂). Among the three proposed novel methods, VMD-CCA decomposed with 15 intrinsic mode functions (IMFs) has shown the best denoising performance for EEG signals producing an average 𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥 and 𝜂𝜂 values of 23.81 dB and 57.01%, respectively for all 23 EEG recordings. On the other hand, for the available 16 fNIRS recordings, VMD-CCA using 10 IMFs produced an average 𝛥𝛥𝛥𝛥𝛥𝛥𝛥𝛥 and 𝜂𝜂 values of 15.97 dB and 39.01%, respectively. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.

Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis

“…That is why choosing 𝜌 𝑐𝑙𝑒𝑎𝑛 = 1 would give a worst-case scenario result. Also, this same formula is used in [40][41][42] assuming the ideal "reference ground truth signal".…”

“…al. [42] utilized variational mode decomposition (VMD) [43] for the correction of motion artifacts from EEG signals.…”

“…A hybrid algorithm was proposed in [53] to filter out the movement artifacts from fNIRS signals where both the spline interpolation method and Savitzky-Golay filtering were employed. Very recently, the two-stage VMD-CCA technique was employed in [42].…”

Deep Learning Technique to Denoise Electromyogram Artifacts from Single-Channel Electroencephalogram Signals

“…In addition to the aforementioned performance metrics, the average power ratio of each EEG frequency band (delta [1-4 Hz], theta [4][5][6][7][8], alpha [8][9][10][11][12][13], beta [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], and gamma [30-80 Hz] bands) to the whole band (1-80 Hz) is also computed and reported for the EMG contaminated EEG, ground truth EEG, and predicted EEG segments.…”

“…The EMG artifacts-free EEG signal can be obtained using either the inverse Fourier transform or the inverse wavelet transform. The use of Wiener filter [13], adaptive filter [14], Hilbert-Huang Transformation (HHT) [15], empirical mode decomposition (EMD) [16], variational mode decomposition (VMD) [17], independent component analysis (ICA) [18], and canonical correlation analysis (CCA) [19] etc. are some other methods for EEG denoising.…”