Wavelet Domain Optimized Savitzky–Golay Filter for the Removal of Motion Artifacts From EEG Recordings

Gajbhiye, Pranjali; Mingchinda, Nopparada; Chen, Wei; Mukhopadhyay, Subhas Chandra; Wilaiprasitporn, Theerawit; Tripathy, Rajesh Kumar

doi:10.1109/tim.2020.3041099

Cited by 44 publications

(17 citation statements)

References 49 publications

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“…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".…”

Section: Discussionmentioning

confidence: 99%

“…Maddirala and Shaik [37] used singular spectrum analysis (SSA) [38] whereas DWT along with the thresholding technique was utilized in [39]. Gajbhiye et al [40] employed wavelet-based transform along with the total variation (TV) and weighted TV (WTV) denoising techniques whereas in [41], wavelet domain optimized Savitzky-Golay filter was proposed for the removal of motion artifacts from EEG. .…”

Section: Introductionmentioning

confidence: 99%

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

Hossain

Chowdhury

Reaz

et al. 2022

Sensors

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The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average and values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average (16.55 dB, utilizing db1 wavelet packet) and largest average (41.40%, using fk8 wavelet packet). The highest average and using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both and values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Hossain

Chowdhury

Reaz

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Components containing artifacts (i.e., eye movements, eye blinks, muscular activity, etc.) were identified and removed using a combination of Savitzky-Golay filter and wavelet thresholding [6 , 7] . Artifacts are signals caused by muscle movements and eye movements which corrupt the original EEG signal.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

SAM 40: Dataset of 40 subject EEG recordings to monitor the induced-stress while performing Stroop color-word test, arithmetic task, and mirror image recognition task

et al. 2022

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This paper presents a collection of electroencephalogram (EEG) data recorded from 40 subjects (female: 14, male: 26, mean age: 21.5 years). The dataset was recorded from the subjects while performing various tasks such as Stroop color-word test, solving arithmetic questions, identification of symmetric mirror images, and a state of relaxation. The experiment was primarily conducted to monitor the short-term stress elicited in an individual while performing the aforementioned cognitive tasks. The individual tasks were carried out for 25 s and were repeated to record three trials. The EEG was recorded using a 32-channel Emotiv Epoc Flex gel kit. The EEG data were then segmented into non-overlapping epochs of 25 s depending on the various tasks performed by the subjects. The EEG data were further processed to remove the baseline drifts by subtracting the average trend obtained using the Savitzky-Golay filter. Furthermore, the artifacts were also removed from the EEG data by applying wavelet thresholding. The dataset proposed in this paper can aid and support the research activities in the field of brain-computer interface and can also be used in the identification of patterns in the EEG data elicited due to stress.

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“…To filter out motion artifacts from the EEG signal, DWT along with the thresholding technique was utilized in [41]. Recently, to reduce motion artifacts from the EEG signals Gajbhiye et al [42] employed wavelet-based transform along with the total variation (TV) and weighted TV (WTV) denoising techniques whereas in [43], wavelet domain optimized Savitzky-Golay filter has been incorporated.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Wavelet Domain Optimized Savitzky–Golay Filter for the Removal of Motion Artifacts From EEG Recordings

Cited by 44 publications

References 49 publications

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

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

SAM 40: Dataset of 40 subject EEG recordings to monitor the induced-stress while performing Stroop color-word test, arithmetic task, and mirror image recognition task

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

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