Feature Extraction from Regenerated EEG – A Better Approach for ICA Based Eye Blink Artifact Detection
Abstract
Purpose: Independent Component Analysis (ICA) decomposition is a commonly used technique for eye blink artifact detection from Electroencephalogram (EEG) signals. Feature extraction from the decomposed ICs is a prime step for blink detection. This paper presents a new model of eye blink detection for ICA based approach, where the decomposed ICs are projected to their corresponding EEG segments (ReEEG), and feature extraction is performed on the ReEEG instead of the IC. ReEEG represents the eye blink activity more distinctly. Hence, ReEEG-based feature extraction is more potential in detecting eye blink artifacts than the traditional IC-based feature extraction.
Materials and Methods: This paper employs twelve EEG features to substantiate the superiority of ReEEG over IC. Support Vector Machine (SVM) is used as a classifier. A dataset, having 2638 clinical EEG epochs, is employed. All the considered twelve features are extracted from ReEEG and fed to SVM one at a time for blink detection. Then the obtained results are compared with an IC-based model with the same features.
Results: The comparison reveals the success of the proposed ReEEG-based blink detection approach over the traditional IC-based approach. Accuracy, precision, recall, and f1 scores are calculated as performance measuring metrics. For almost all features, ReEEG-based approach achieved up to 12.25% higher accuracy, 24.95% higher precision, 13.49% higher recall, and 12.89% higher f1 score than the IC-based traditional method.
Conclusion: The proposed model will be useful for researchers in dealing with the eye blink artifacts of EEG signals with more efficacy.
1- Carlo Miniussi and Gregor Thut, "Combining TMS and EEG offers new prospects in cognitive neuroscience." Brain topography, Vol. 22pp. 249-56, (2010).
2- Steven M Peterson, Emily Furuichi, and Daniel P Ferris, "Effects of virtual reality high heights exposure during beam-walking on physiological stress and cognitive loading." PloS one, Vol. 13 (No. 7), p. e0200306, (2018).
3- Ruxandra I Tivadar and Micah M Murray, "A primer on electroencephalography and event-related potentials for organizational neuroscience." Organizational Research Methods, Vol. 22 (No. 1), pp. 69-94, (2019).
4- Swati Aggarwal and Nupur Chugh, "Review of machine learning techniques for EEG based brain computer interface." Archives of Computational Methods in Engineering, pp. 1-20, (2022).
5- ZT Al-Qaysi, BB Zaidan, AA Zaidan, and MS Suzani, "A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations." Computer methods and programs in biomedicine, Vol. 164pp. 221-37, (2018).
6- Rihab Bousseta, I El Ouakouak, M Gharbi, and F Regragui, "EEG based brain computer interface for controlling a robot arm movement through thought." Irbm, Vol. 39 (No. 2), pp. 129-35, (2018).
7- Anirban Chowdhury, Haider Raza, Yogesh Kumar Meena, Ashish Dutta, and Girijesh Prasad, "An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation." Journal of neuroscience methods, Vol. 312pp. 1-11, (2019).
8- Ibrahim Kaya, "A brief summary of EEG artifact handling." Brain-computer interface, (No. 9), (2019).
9- Suguru Kanoga, Atsunori Kanemura, and Hideki Asoh, "Multi-scale dictionary learning for ocular artifact reduction from single-channel electroencephalograms." Neurocomputing, Vol. 347pp. 240-50, (2019).
10- Souvik Phadikar, Nidul Sinha, and Rajdeep Ghosh, "Automatic eyeblink artifact removal from EEG signal using wavelet transform with heuristically optimized threshold." IEEE Journal of Biomedical and Health Informatics, Vol. 25 (No. 2), pp. 475-84, (2020).
11- Mohammad Shahbakhti, Maxime Maugeon, Matin Beiramvand, and Vaidotas Marozas, "Low complexity automatic stationary wavelet transform for elimination of eye blinks from EEG." Brain sciences, Vol. 9 (No. 12), p. 352, (2019).
12- Rakesh Ranjan, Bikash Chandra Sahana, and Ashish Kumar Bhandari, "Ocular artifact elimination from electroencephalography signals: A systematic review." Biocybernetics and Biomedical Engineering, Vol. 41 (No. 3), pp. 960-96, (2021).
13- Malik Muhammad Naeem Mannan, Muhammad Ahmad Kamran, and Myung Yung Jeong, "Identification and removal of physiological artifacts from electroencephalogram signals: A review." Ieee Access, Vol. 6pp. 30630-52, (2018).
14- Sari Sadiya, Tuka Alhanai, and Mohammad M Ghassemi, "Artifact detection and correction in eeg data: a review." in 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), (2021): IEEE, pp. 495-98.
15- Md Kafiul Islam, Amir Rastegarnia, and Zhi Yang, "Les méthodes de détection et de rejet d'artefact de l'EEG de scalp: revue de littérature." Neurophysiologie Clinique, Vol. 46 (No. 4-5), pp. 287-305, (2016).
16- Maliha Rashida and Mohammad Ashfak Habib, "Quantitative EEG features and machine learning classifiers for eye-blink artifact detection: A comparative study." Neuroscience Informatics, Vol. 3 (No. 1), p. 100115, (2023).
17- Priyalakshmi Sheela and Subha D Puthankattil, "A hybrid method for artifact removal of visual evoked EEG." Journal of neuroscience methods, Vol. 336p. 108638, (2020).
18- Raheel Zafar, Abdul Qayyum, and Wajid Mumtaz, "Automatic eye blink artifact removal for EEG based on a sparse coding technique for assessing major mental disorders." Journal of Integrative Neuroscience, Vol. 18 (No. 3), pp. 217-29, (2019).
19- Alejandro Villena, Lorenzo J Tardón, Isabel Barbancho, Ana M Barbancho, Elvira Brattico, and Niels T Haumann, "Preprocessing for lessening the influence of eye artifacts in EEG analysis." Applied Sciences, Vol. 9 (No. 9), p. 1757, (2019).
20- Thea Radüntz, Jon Scouten, Olaf Hochmuth, and Beate Meffert, "Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features." Journal of neural engineering, Vol. 14 (No. 4), p. 046004, (2017).
21- Jun Feng Gao, Yong Yang, Pan Lin, Pei Wang, and Chong Xun Zheng, "Automatic removal of eye-movement and blink artifacts from EEG signals." Brain topography, Vol. 23pp. 105-14, (2010).
22- Salim Çınar, "Design of an automatic hybrid system for removal of eye-blink artifacts from EEG recordings." Biomedical Signal Processing and Control, Vol. 67p. 102543, (2021).
23- Mohamed F Issa and Zoltan Juhasz, "Improved EOG artifact removal using wavelet enhanced independent component analysis." Brain sciences, Vol. 9 (No. 12), p. 355, (2019).
24- Ajay Kumar Maddirala and Kalyana C Veluvolu, "ICA with CWT and k-means for Eye-blink Artifact Removal from Fewer Channel EEG." IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 30pp. 1361-73, (2022).
25- Xiao Jiang, Gui-Bin Bian, and Zean Tian, "Removal of artifacts from EEG signals: a review." Sensors, Vol. 19 (No. 5), p. 987, (2019).
26- Elena Nedelcu, Raluca Portase, Ramona Tolas, Raul Muresan, Mihaela Dinsoreanu, and Rodica Potolea, "Artifact detection in EEG using machine learning." in 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), (2017): IEEE, pp. 77-83.
27- Jianhui Wang, Jiuwen Cao, Dinghan Hu, Tiejia Jiang, and Feng Gao, "Eye blink artifact detection with novel optimized multi-dimensional electroencephalogram features." IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 29pp. 1494-503, (2021).
28- K Yasoda, RS Ponmagal, KS Bhuvaneshwari, and K Venkatachalam, "Automatic detection and classification of EEG artifacts using fuzzy kernel SVM and wavelet ICA (WICA)." Soft Computing, Vol. 24pp. 16011-19, (2020).
29- Omid Dehzangi, Alexander Melville, and Mojtaba Taherisadr, "Automatic eeg blink detection using dynamic time warping score clustering." in Advances in Body Area Networks I: Post-Conference Proceedings of BodyNets 2017, (2019): Springer, pp. 49-60.
30- Suguru Kanoga, Masaki Nakanishi, and Yasue Mitsukura, "Assessing the effects of voluntary and involuntary eyeblinks in independent components of electroencephalogram." Neurocomputing, Vol. 193pp. 20-32, (2016).
31- Dominic Langlois, Sylvain Chartier, and Dominique Gosselin, "An introduction to independent component analysis: InfoMax and FastICA algorithms." Tutorials in Quantitative Methods for Psychology, Vol. 6 (No. 1), pp. 31-38, (2010).
32- Arnaud Delorme and Scott Makeig, "EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis." Journal of neuroscience methods, Vol. 134 (No. 1), pp. 9-21, (2004).
2- Steven M Peterson, Emily Furuichi, and Daniel P Ferris, "Effects of virtual reality high heights exposure during beam-walking on physiological stress and cognitive loading." PloS one, Vol. 13 (No. 7), p. e0200306, (2018).
3- Ruxandra I Tivadar and Micah M Murray, "A primer on electroencephalography and event-related potentials for organizational neuroscience." Organizational Research Methods, Vol. 22 (No. 1), pp. 69-94, (2019).
4- Swati Aggarwal and Nupur Chugh, "Review of machine learning techniques for EEG based brain computer interface." Archives of Computational Methods in Engineering, pp. 1-20, (2022).
5- ZT Al-Qaysi, BB Zaidan, AA Zaidan, and MS Suzani, "A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations." Computer methods and programs in biomedicine, Vol. 164pp. 221-37, (2018).
6- Rihab Bousseta, I El Ouakouak, M Gharbi, and F Regragui, "EEG based brain computer interface for controlling a robot arm movement through thought." Irbm, Vol. 39 (No. 2), pp. 129-35, (2018).
7- Anirban Chowdhury, Haider Raza, Yogesh Kumar Meena, Ashish Dutta, and Girijesh Prasad, "An EEG-EMG correlation-based brain-computer interface for hand orthosis supported neuro-rehabilitation." Journal of neuroscience methods, Vol. 312pp. 1-11, (2019).
8- Ibrahim Kaya, "A brief summary of EEG artifact handling." Brain-computer interface, (No. 9), (2019).
9- Suguru Kanoga, Atsunori Kanemura, and Hideki Asoh, "Multi-scale dictionary learning for ocular artifact reduction from single-channel electroencephalograms." Neurocomputing, Vol. 347pp. 240-50, (2019).
10- Souvik Phadikar, Nidul Sinha, and Rajdeep Ghosh, "Automatic eyeblink artifact removal from EEG signal using wavelet transform with heuristically optimized threshold." IEEE Journal of Biomedical and Health Informatics, Vol. 25 (No. 2), pp. 475-84, (2020).
11- Mohammad Shahbakhti, Maxime Maugeon, Matin Beiramvand, and Vaidotas Marozas, "Low complexity automatic stationary wavelet transform for elimination of eye blinks from EEG." Brain sciences, Vol. 9 (No. 12), p. 352, (2019).
12- Rakesh Ranjan, Bikash Chandra Sahana, and Ashish Kumar Bhandari, "Ocular artifact elimination from electroencephalography signals: A systematic review." Biocybernetics and Biomedical Engineering, Vol. 41 (No. 3), pp. 960-96, (2021).
13- Malik Muhammad Naeem Mannan, Muhammad Ahmad Kamran, and Myung Yung Jeong, "Identification and removal of physiological artifacts from electroencephalogram signals: A review." Ieee Access, Vol. 6pp. 30630-52, (2018).
14- Sari Sadiya, Tuka Alhanai, and Mohammad M Ghassemi, "Artifact detection and correction in eeg data: a review." in 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), (2021): IEEE, pp. 495-98.
15- Md Kafiul Islam, Amir Rastegarnia, and Zhi Yang, "Les méthodes de détection et de rejet d'artefact de l'EEG de scalp: revue de littérature." Neurophysiologie Clinique, Vol. 46 (No. 4-5), pp. 287-305, (2016).
16- Maliha Rashida and Mohammad Ashfak Habib, "Quantitative EEG features and machine learning classifiers for eye-blink artifact detection: A comparative study." Neuroscience Informatics, Vol. 3 (No. 1), p. 100115, (2023).
17- Priyalakshmi Sheela and Subha D Puthankattil, "A hybrid method for artifact removal of visual evoked EEG." Journal of neuroscience methods, Vol. 336p. 108638, (2020).
18- Raheel Zafar, Abdul Qayyum, and Wajid Mumtaz, "Automatic eye blink artifact removal for EEG based on a sparse coding technique for assessing major mental disorders." Journal of Integrative Neuroscience, Vol. 18 (No. 3), pp. 217-29, (2019).
19- Alejandro Villena, Lorenzo J Tardón, Isabel Barbancho, Ana M Barbancho, Elvira Brattico, and Niels T Haumann, "Preprocessing for lessening the influence of eye artifacts in EEG analysis." Applied Sciences, Vol. 9 (No. 9), p. 1757, (2019).
20- Thea Radüntz, Jon Scouten, Olaf Hochmuth, and Beate Meffert, "Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features." Journal of neural engineering, Vol. 14 (No. 4), p. 046004, (2017).
21- Jun Feng Gao, Yong Yang, Pan Lin, Pei Wang, and Chong Xun Zheng, "Automatic removal of eye-movement and blink artifacts from EEG signals." Brain topography, Vol. 23pp. 105-14, (2010).
22- Salim Çınar, "Design of an automatic hybrid system for removal of eye-blink artifacts from EEG recordings." Biomedical Signal Processing and Control, Vol. 67p. 102543, (2021).
23- Mohamed F Issa and Zoltan Juhasz, "Improved EOG artifact removal using wavelet enhanced independent component analysis." Brain sciences, Vol. 9 (No. 12), p. 355, (2019).
24- Ajay Kumar Maddirala and Kalyana C Veluvolu, "ICA with CWT and k-means for Eye-blink Artifact Removal from Fewer Channel EEG." IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 30pp. 1361-73, (2022).
25- Xiao Jiang, Gui-Bin Bian, and Zean Tian, "Removal of artifacts from EEG signals: a review." Sensors, Vol. 19 (No. 5), p. 987, (2019).
26- Elena Nedelcu, Raluca Portase, Ramona Tolas, Raul Muresan, Mihaela Dinsoreanu, and Rodica Potolea, "Artifact detection in EEG using machine learning." in 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), (2017): IEEE, pp. 77-83.
27- Jianhui Wang, Jiuwen Cao, Dinghan Hu, Tiejia Jiang, and Feng Gao, "Eye blink artifact detection with novel optimized multi-dimensional electroencephalogram features." IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 29pp. 1494-503, (2021).
28- K Yasoda, RS Ponmagal, KS Bhuvaneshwari, and K Venkatachalam, "Automatic detection and classification of EEG artifacts using fuzzy kernel SVM and wavelet ICA (WICA)." Soft Computing, Vol. 24pp. 16011-19, (2020).
29- Omid Dehzangi, Alexander Melville, and Mojtaba Taherisadr, "Automatic eeg blink detection using dynamic time warping score clustering." in Advances in Body Area Networks I: Post-Conference Proceedings of BodyNets 2017, (2019): Springer, pp. 49-60.
30- Suguru Kanoga, Masaki Nakanishi, and Yasue Mitsukura, "Assessing the effects of voluntary and involuntary eyeblinks in independent components of electroencephalogram." Neurocomputing, Vol. 193pp. 20-32, (2016).
31- Dominic Langlois, Sylvain Chartier, and Dominique Gosselin, "An introduction to independent component analysis: InfoMax and FastICA algorithms." Tutorials in Quantitative Methods for Psychology, Vol. 6 (No. 1), pp. 31-38, (2010).
32- Arnaud Delorme and Scott Makeig, "EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis." Journal of neuroscience methods, Vol. 134 (No. 1), pp. 9-21, (2004).
| Files | ||
| Issue | Vol 11 No 2 (2024) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v11i2.15336 | |
| Keywords | ||
| Electroencephalogram Eye Blink Artifact Independent Component Analysis Support Vector Machine Feature Extraction | ||
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How to Cite
1.
Rashida M, Habib MA. Feature Extraction from Regenerated EEG – A Better Approach for ICA Based Eye Blink Artifact Detection. Frontiers Biomed Technol. 2023;11(2):199-206.
