EYE BLINK IDENTIFICATION AND REMOVAL FROM SINGLE-CHANNEL EEG USING EMD WITH ENERGY THRESHOLD AND ADAPTIVE FILTER

Lutfi Aizat Mohamed Jefri; Faridah Abd. Rahman; Noreha Abdul Malik; Farah Nadia Mohd Isa

doi:10.31436/iiumej.v24i2.2814

Authors

Lutfi Aizat Mohamed Jefri International Islamic University Malaysia https://orcid.org/0009-0007-1356-6256
Faridah Abd. Rahman International Islamic University Malaysia https://orcid.org/0000-0002-7683-7419
Noreha Abdul Malik International Islamic University Malaysia
Farah Nadia Mohd Isa International Islamic University Malaysia https://orcid.org/0000-0002-4305-4151

DOI:

https://doi.org/10.31436/iiumej.v24i2.2814

Keywords:

empirical mode decomposition(EMD), adaptive filter, energy thresholding, Eye blink extraction, Univariate processing

Abstract

Electroencephalography (EEG) is a non-invasive method for measuring electrical activity in the brain, which reflects the underlying neural activity of the brain. In recent years, portable EEG devices become more ubiquitous in domestic uses, research and clinical applications due to their compact design and ease of use in various settings. Like many other biosignal modalities, EEG devices are prone to the interference of physiological artifacts, mainly from eye blinking. However, since portable EEGs are equipped with only a few channels at most or sometimes just a single channel, removing the eye blink artifact from the EEG data is a challenge. The conventional artifact removal method using source separation cannot be applied to a single-channel EEG signal. Eye blink artifact removal is important because its spectrum overlaps with the EEG’s theta and delta frequency bands, which can be confused with brain activity. Univariate-based removal method is compatible with EEG data with few channels. This paper presents a method to remove eye blink artifact based on single-channel EEG processing using Empirical Mode Decomposition (EMD) and Adaptive Noise Cancellation (ANC) system. By applying energy thresholds in EMD, there is no need to incorporate EMD with other methods to extract eye blink component accurately. ANC is used to converge the extracted eye blink component for effective eye blink artifact removal with very minimal changes to affected EEG data. The proposed method was tested on simulated EEG signals, and the result showed a good Root Mean-Square Error (RMSE) average value of the cleaned EEG ( ) and a high Correlation Coefficient (CC) average value of the cleaned EEG ( ).

ABSTRAK: Electroensefalografi (EEG) adalah kaedah bukan invasif untuk mengukur aktiviti elektrik di dalam otak, yang mencerminkan aktiviti saraf dalam otak. Kebelakangan ini, peranti EEG mudah alih menjadi lebih meluas dalam kegunaan domestik, penyelidikan dan aplikasi klinikal kerana reka bentuknya yang padat dan kemudahan penggunaan dalam pelbagai tetapan. Seperti kebanyakan modaliti biosignal yang lain, peranti EEG terdedah kepada gangguan artifak fisiologi, terutamanya daripada mata kerdipan mata. Walau bagaimanapun, memandangkan EEG mudah alih dilengkapi dengan paling banyak pun hanya beberapa saluran, atau kadangkala hanya satu saluran, mengalih keluar artifak kerdipan mata daripada data EEG adalah satu cabaran. Kaedah penyingkiran artifak konvensional menggunakan pemisahan sumber tidak dapat digunakan pada alat EEG satu saluran. Penyingkiran artifak berkelip mata adalah penting kerana spektrumnya bertindih dengan jalur frekuensi theta dan delta EEG, maka boleh dikelirukan dengan aktiviti otak. Kaedah penyingkiran berasaskan univariat adalah serasi untuk data EEG dengan saluran yang sedikit. Kertas kerja ini membentangkan kaedah untuk membuang artifak kelip mata berdasarkan pemprosesan EEG saluran tunggal menggunakan Penguraian Mod Empirikal (EMD) dan Pembatalan Bunyi Adaptif (ANC). Dengan menggunakan ambang tenaga dalam EMD, tiada keperluan untuk menggabungkan EMD dengan kaedah lain bagi mengekstrak komponen kerdipan mata dengan tepat. ANC digunakan untuk menumpu komponen kerdipan mata yang diekstrak bagi penyingkiran artifak kerdipan mata yang berkesan dengan perubahan yang sangat minimum pada data EEG yang terjejas. Kaedah yang dicadangkan telah diuji pada signal EEG yang disimulasi, serta hasilnya menunjukkan nilai purata Ralat Min Kuasa Dua Purata (RMSE) yang baik bagi EEG yang dibersihkan (0.3211±0.2738), dan nilai purata Pekali Korelasi (CC) yang baik bagi EEG yang dibersihkan (0.9430±0.0839).

ABSTRAK: Sabut gentian kelapa sawit berpotensi sebagai bahan mentah biojisim lignoselulosa bagi menambah nilai produk biojisim seperti bahan bio api generasi kedua, biokomposit atau biotenaga. Walau bagaimanapun, komposisi lignin yang wujud dalam biojisim lignoselulosa menentang proses tambah nilai dan melindungi komposisi selulosa, dengan itu mengehadkan penukaran selulosa kepada produk yang lebih berharga. Kaedah hibrid ozonasi-ultrasonik sebagai kaedah merendahkan lignin, mula mendapat perhatian sebagai kaedah berkesan. Selain itu, Reka Bentuk Kotak-Behnken (BBD) telah digunakan bagi menyiasat setiap kesan pembolehubah bebas pada keadaan proses prarawatan menggunakan kaedah permukaan tindak balas (RSM), iaitu masa tindak balas (30-90) min, suhu tindak balas (20 -40) ^oC dan kadar aliran ozon (1-3) L/min terhadap tindak balas pada peratusan degradasi lignin (%). Keadaan optimum bagi proses prarawatan ditentukan menggunakan graf fungsi keboleh inginan. Dapatan kajian menunjukkan bahawa masa tindak balas, suhu tindak balas, dan kadar aliran ozon mempunyai kesan yang signifikan terhadap degradasi lignin (p<0.05). Keadaan optimum peratusan degradasi lignin tertinggi adalah pada 92.08% pada suhu tindak balas 30 ^oC dengan kadar aliran ozon 2 L/min selama 60 minit masa tindak balas. Penurunan puncak penyerapan lignin pada 1638 cm^-1 dan 1427 cm^-1 disokong oleh keputusan analisis peningkatan kehabluran sampel selepas prarawatan degradasi lignin sebanyak 80.20% dan telah disahkan oleh perubahan morfologi sabut gentian selepas proses prarawatan menunjukkan bahawa sebatian lignin telah berjaya didegradasi daripada produk selulosa sabut gentian.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Artoni F, Fanciullacci C, Bertolucci F, Panarese A, Makeig S, Micera S, Chisari C. (2017). Unidirectional brain to muscle connectivity reveals motor cortex control of leg muscles during stereotyped walking. Neuroimage, 159:403–416. doi: 10.1016/j.neuroimage.2017.07.013. DOI: https://doi.org/10.1016/j.neuroimage.2017.07.013

Mandekar S, Holland A, Thielen M, Behbahani M, Melnykowycz M (2022). Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG. Sensors, 22(4):1568. https://doi.org/10.3390/s22041568 DOI: https://doi.org/10.3390/s22041568

Lau-Zhu A, Lau MPH, McLoughlin G (2019). Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges. Dev Cogn Neurosci., 36:100635. doi: 10.1016/j.dcn.2019.100635. DOI: https://doi.org/10.1016/j.dcn.2019.100635

Nagar P and Sethia D (2019). Brain Mapping Based Stress Identification Using Portable EEG Based Device. 11th International Conference on Communication Systems and Networks (COMSNETS), 601–606. doi: 10.1109/COMSNETS.2019.8711009. DOI: https://doi.org/10.1109/COMSNETS.2019.8711009

Bhavnani S, Parameshwaran D, Sharma KK, Mukherjee D, Divan G, Patel V and Thiagarajan TC. (2022). The Acceptability, Feasibility, and Utility of Portable Electroencephalography to Study Resting-State Neurophysiology in Rural Communities. Front. Hum. Neurosci, 16:802764. doi: 10.3389/fnhum.2022.802764. DOI: https://doi.org/10.3389/fnhum.2022.802764

Sawangjai P, Hompoonsup S, Leelaarporn P, Kongwudhikunakorn S, and Wilaiprasitporn T. (2020). Consumer Grade EEG Measuring Sensors as Research Tools: A Review. IEEE Sensors Journal, 20(8):3996–4024. doi: 10.1109/JSEN.2019.2962874. DOI: https://doi.org/10.1109/JSEN.2019.2962874

Shahbakhti M et al. (2021). VME-DWT: An Efficient Algorithm for Detection and Elimination of Eye Blink from Short Segments of Single EEG Channel. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29:408–417. doi: 10.1109/TNSRE.2021.3054733. DOI: https://doi.org/10.1109/TNSRE.2021.3054733

Iwasaki M, Kellinghaus C, Alexopoulos A V, Burgess R C, Kumar A N, Han Y H, Lüders H O, and Leigh R J. (2005). Effects of eyelid closure, blinks, and eye movements on the electroencephalogram. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 116(4), 878–885. https://doi.org/10.1016/j.clinph.2004.11.001. DOI: https://doi.org/10.1016/j.clinph.2004.11.001

Kanoga S, Kanemura A, and Asoh H. (2019). Multi-scale dictionary learning for ocular artifact reduction from single-channel electroencephalograms. Neurocomputing, 347:240–250. doi: 10.1016/j.neucom.2019.02.060. DOI: https://doi.org/10.1016/j.neucom.2019.02.060

Agarwal M and Sivakumar R. (2019). Blink: A Fully Automated Unsupervised Algorithm for Eye-Blink Detection in EEG Signals. 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton, 1113–1121. doi: 10.1109/ALLERTON.2019.8919795. DOI: https://doi.org/10.1109/ALLERTON.2019.8919795

G Bhaskar N Rao, Anumala V S, Peri P P and Aneesh S. (2020). Automatic Detection and Correction of Blink Artifacts in Single Channel EEG Signals. International Journal of Advanced Computer Science and Applications (IJACSA), 11(1). http://dx.doi.org/10.14569/IJACSA.2020.0110144 DOI: https://doi.org/10.14569/IJACSA.2020.0110144

Jefri L A M, Rahman F A, Malik N A, and Isa F N M. (2022). Eye blink region detection in EEG using amplitude thresholding. 8th International Conference on Mechatronics Engineering (ICOM 2022), 77–83. doi: 10.1049/icp.2022.2269. DOI: https://doi.org/10.1049/icp.2022.2269

Maddirala A K and Veluvolu K C. (2022). SSA with CWT and k-Means for Eye-Blink Artifact Removal from Single-Channel EEG Signals. Sensors, 22(3). doi: 10.3390/s22030931. DOI: https://doi.org/10.3390/s22030931

Maddirala A K and Veluvolu K C. (2021). Eye-blink artifact removal from single channel EEG with k-means and SSA. Sci Rep., 11(1). doi: 10.1038/s41598-021-90437-7. DOI: https://doi.org/10.1038/s41598-021-90437-7

Zeiler A, Faltermeier R, Keck I R, Tomé A M, Puntonet C G, and Lang E W. (2010). Empirical mode decomposition - An introduction. Proceedings of the International Joint Conference on Neural Networks, 2010. doi: 10.1109/IJCNN.2010.5596829. DOI: https://doi.org/10.1109/IJCNN.2010.5596829

Shahbakhti M, Bavi M and Eslamizadeh M. (2013). Elimination of Blink from EEG By Adaptive Filtering without Using Artifact Reference. 2013 4th International Conference on Intelligent Systems, Modelling and Simulation, 190-194, doi: 10.1109/ISMS.2013.106. DOI: https://doi.org/10.1109/ISMS.2013.106

Patel R, Janawadkar M P R, Sengottuvel S, Gireesan K, and Radhakrishnan T S. (2016). Suppression of Eye-Blink Associated Artifact Using Single Channel EEG Data by Combining Cross-Correlation with Empirical Mode Decomposition. IEEE Sens J., 16(18):6947–6954. doi: 10.1109/JSEN.2016.2591580. DOI: https://doi.org/10.1109/JSEN.2016.2591580

Molla M K I, Rabiul Islam M, Tanaka T, and Rutkowski T M. (2012). Artifact suppression from EEG signals using data adaptive time domain filtering. Neurocomputing, 97:297–308. doi: 10.1016/j.neucom.2012.05.009. DOI: https://doi.org/10.1016/j.neucom.2012.05.009

Egambaram A, Badruddin N, Asirvadam V S, Begum T, Fauvet E, and Stolz C. (2020). FastEMD–CCA algorithm for unsupervised and fast removal of eyeblink artifacts from electroencephalogram. Biomed Signal Process Control, 57:2020. doi: 10.1016/j.bspc.2019.101692. DOI: https://doi.org/10.1016/j.bspc.2019.101692

Rato R T, Ortigueira M D, and Batista A G. (2008). On the HHT, its problems, and some solutions. Mech Syst Signal Process, 22(6):13 DOI: https://doi.org/10.1016/j.ymssp.2007.11.028