Automatic Eyeblink Artifact Removal From EEG Signal Using Wavelet Transform With Heuristically Optimized Threshold

Phadikar, Souvik; Sinha, Nidul; Ghosh, Rajdeep

doi:10.1109/jbhi.2020.2995235

Cited by 66 publications

(23 citation statements)

References 31 publications

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“…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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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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“…Machine learning based methods become favored choices in eye blink and EEG artifact detection in the past [12][13][14][15][16][17][18]. For instances, in [12], the genetic algorithm was developed for multi-dimensional candidate EEG features optimization and the Parzen window detector was adopted for eye blink artifact detection.…”

Section: Introductionmentioning

confidence: 99%

Eye Blink Artifact Detection With Novel Optimized Multi-Dimensional Electroencephalogram Features

Wang

Cao

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Accurate eye blink artifact detection is essential for electroencephalogram (EEG) analysis and auxiliary analysis of nervous system diseases, especially in the presence of the frontal epileptiform discharges. In this paper, we develop a novel eye blink artifact detection algorithm based on optimally selected multi-dimensional EEG features. Specific efforts have been paid to filtering the frontal epileptiform discharges, where an unsupervised learning exploiting the EEG signal physiological characteristics and smooth nonlinear energy operator (SNEO) based on the K-means clustering has been firstly proposed. Multiple statistical EEG features derived from the frontal electrodes and other electrodes are then extracted to characterize eye blink artifacts. Discriminative feature selection scheme based on the variance filtering and Relief algorithms has been respectively studied, and the average correlation coefficient (ACC) is applied for feature optimization evaluation. The eye blink artifact detection is finally achieved based on the support vector machine (SVM) trained on the optimized EEG features. The effectiveness of the proposed algorithm is demonstrated by experiments carried out on the EEG database of 11 subjects recorded from the Children's Hospital, Zhejiang University School of Medicine (CHZU). Comparisons to several state-of-the-art (SOTA) eye blink artifact detection methods are also presented.

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“…More complicated frameworks have also been designed to find feature subsets with better prediction performances, e.g., embedded [13] and meta-heuristic [14] feature selection algorithms. Swarm intelligence (SI) is a type of meta-heuristic feature selection algorithm that imitates living organisms' behaviors to generate intermediate feature subsets for performance evaluations [15]. An SI feature selection algorithm extracts the living organisms' behaviors as abstract algorithmic operations for feature subsets, including genetic information exchanges and dynamic searching strategies [16].…”

Section: Introductionmentioning

confidence: 99%

Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

Han

Huang

Zhou

2021

Genes

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Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a better prediction model. The hidden patterns in the FS solution space make it challenging to achieve a feature subset with satisfying prediction performances. Swarm intelligence (SI) algorithms mimic the target searching behaviors of various animals and have demonstrated promising capabilities in selecting features with good machine learning performances. Our study revealed that different SI-based feature selection algorithms contributed complementary searching capabilities in the FS solution space, and their collaboration generated a better feature subset than the individual SI feature selection algorithms. Nine SI-based feature selection algorithms were integrated to vote for the selected features, which were further refined by the dynamic recursive feature elimination framework. In most cases, the proposed Zoo algorithm outperformed the existing feature selection algorithms on transcriptomics and methylomics datasets.

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Automatic Eyeblink Artifact Removal From EEG Signal Using Wavelet Transform With Heuristically Optimized Threshold

Cited by 66 publications

References 31 publications

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

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

Eye Blink Artifact Detection With Novel Optimized Multi-Dimensional Electroencephalogram Features

Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

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