Abstract
Mental stress is an issue that creates functional limitations in the workplace. Chronic stress leads to a number of psychophysiological sicknesses. For instance, it raises the risk of depression, heart attack, and stroke. According to the most recent findings in neuroscience, the human brain is the primary focus of mental stress. Perception of biological motion in the human brain determines the risky and stressful situations. Neural signaling of the human brain is used as an objective measure for determining the stress level of a subject. The oscillations of electroencephalography (EEG) signals are utilized for classifying human stress. EEG signals have a higher temporal resolution and are rapidly distorted with unwanted noise, resulting in a variety of artifacts. This study utilizes Extended Independent Component Analysis based approach for artifacts removal. A Multiclass Common Spatial Pattern-based moving window technique is proposed here to obtain the most distinguishable time segment of EEG trials. BiLSTM is used to improve classification results. In order to validate the model performance, two publically available datasets (i.e., DEAP and SEED) are utilized. Employing these datasets, the proposed model achieves state-of-the-art results (93.1, 96.84%) for EEG signal classification to identify stress.
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References
Ang KK, Chin ZY, Wang C, Guan C, Zhang H (2012a) Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci 6:39
Ang KK, Chin ZY, Wang C, Guan C, Zhang H (2012b) Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci 6(39):1–9
Arsalan A, Majid M, Butt AR, Anwar SM (2019) Classification of perceived mental stress using a commercially available EEG headband. IEEE J Biomed Health Inform 23(6):2257–2264
Bhavsar R, Sun Y, Helian N, Davey N, Mayor D, Steffert T (2018) The correlation between EEG signals as measured in different positions on scalp varying with distance. Procedia Comp Sci 123:92–97
Chen X, Xu X, Liu A, McKeown MJ, Wang ZJ (2017) The use of multivariate EMD and CCA for denoising muscle artifacts from few-channel EEG recordings. IEEE Trans Instrum Meas 67(2):359–370
Chen X, Chen Q, Zhang Y, Wang ZJ (2018) A novel EEMD-CCA approach to removing muscle artifacts for pervasive EEG. IEEE Sens J 19(19):8420–8431
Chen Y, Xue S, Li D, Geng X (2021) The application of independent component analysis in removing the noise of EEG signal. In: 6th International Conference on Smart Grid and Electrical Automation ICSGEA IEEE: 138–141
Dong E, Zhou K, Tong J, Du S (2020) A novel hybrid kernel function relevance vector machine for multi-task motor imagery EEG classification. Biomed Sign Process Control. 60:1–12
Geng X, Li D, Chen H, Yu P, Yan H, Yue M (2021) An improved feature extraction algorithms of EEG signals based on motor imagery brain-computer interface. Alexandria Eng J. 61(6):4807–48020
Giannakakis G, Grigoriadis D, Giannakaki K, Simantiraki O, Roniotis A (2019) Review on psychological stress detection using biosignals. IEEE Transactions on Affective Computing. 1–22
Halim Z, Rehan M (2020) On identification of driving-induced stress using electroencephalogram signals: a framework based on wearable safety-critical scheme and machine learning. Information Fusion 1(53):66–79
Halim Z, Atif M, Rashid A, Edwin CA (2017) Profiling players using real-world datasets: clustering the data and correlating the results with the big-five personality traits. IEEE Trans Affect Comp 10(4):568–584
Halim Z, Waqar M, Tahir M (2020) A machine learning-based investigation utilizing the in-text features for the identification of dominant emotion in an email. Knowl-Based Syst 15(208):1–17
Halim Z, Baig R, Bashir S (2007) Temporal patterns analysis in eeg data using sonification. In: International Conference on Information and Emerging Technologies. IEEE: 1–6
Hasan MJ, Kim JM (2019) A hybrid feature pool-based emotional stress state detection algorithm using EEG signals. Brain Sci 9(12):1–15
Jebelli H, Hwang S, Lee S (2018) EEG-based workers’ stress recognition at construction sites. Autom Constr 1(93):315–324
Khateeb M, Anwar SM, Alnowami M (2021) Multi-domain feature fusion for emotion classification using DEAP dataset. IEEE Access. 9:12134–42
Khosla A, Khandnor P, Chand T (2020) A comparative analysis of signal processing and classification methods for different applications based EEG signals. Biocybern Biomed Eng 40(2):649–690
Koelstra S, Muhl C, Soleymani M, Lee JS, Yazdani A (2011) Deap: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comp 3(1):18–31
Kumar JS, Bhuvaneswar P (2012) Analysis of electroencephalography EEG signals andits categorization a study. Procedia Eng 1(38):2525–2536
Lan Z, Sourina O, Wang L, Scherer R, Müller-Putz GR (2018) Domain adaptation techniques for EEG-based emotion recognition: a comparative study on two public datasets. IEEE Trans Cogn Develop Syst. 11(1):85–94
Liu J, Wu G, Luo Y, Qiu S, Yang S, Li W, Bi Y (2020) EEG-based emotion classification using a deep neural network and sparse autoencoder. Front Syst Neurosci 14:43
Liu A, Song G, Lee S, Fu X, Chen X (2021) A state-dependent IVA model for muscle artifacts removal from EEG recording. IEEE Trans Instr Measur 5(70):1–3
Mannan MM, Kamran MA, Jeong MY (2018) Identification and removal of physiological artifacts from electroencephalogram signals: a review. IEEE Access 31(6):30630–30652
Mukherjee P, Roy AH (2019) Detection of Stress in Human Brain. In: Second International Conference on Advanced Computational and Communication Paradigms ICACCP IEEE. 1–6
Noorbasha SK, Sudha GF (2021) Removal of EOG artifacts and separation of different cerebral activity components from single channel EEG—an efficient approach combining SSA–ICA with wavelet thresholding for BCI applications. Biomed Signal Process Control 1(63):1–12
Oosugi N, Kitajo K, Hasegawa N, Nagasaka Y, Okanoya K (2017) A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal. Neural Netw 1(93):1–6
Rahman MA, Hossain MF, Hossain M, Ahmmed R (2020) Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal. Egyptian Informatics Journal 21(1):23–35
Rashid M, Sulaiman N, Abdul Majeed PP, MusaBari RMBS (2020) Current status, challenges, and possible solutions of EEG-based brain-computer interface: a comprehensive review. Front Neurorobot 14:25
Russell JA (1980) A circumplex model of affect. J Pers Soc Psychol 39(6):1161–1178
Saeidi M, Karwowski W, Farahani FV, Fiok K, Taiar R (2021) Neural decoding of EEG signals with machine learning: a systematic review. Brain Sci 11(11):1–44
Sharma R, Pachori RB, Sircar P (2020) Automated emotion recognition based on higher order statistics and deep learning algorithm. Biomed Signal Process Control. 1(58):101867
Shon D, Im K, Park JH, Lim DS, Jang B, Kim JM (2018) Emotional stress state detection using genetic algorithm-based feature selection on EEG signals. Int J Environ Res Public Health 15(11):2461–2472
Sun L, Liu Y, Beadle PJ (2005) Independent component analysis of EEG signals. In: Proceedings of IEEE International Workshop on VLSI Design and Video Technology IEEE: 219–222
Tang H, Liu W, Zheng WL, Lu BL (2017) Multimodal emotion recognition using deep neural networks. International Conference on Neural Information Processing. Springer, Cham, pp 811–819
Ullah S, Halim Z (2021) Imagined character recognition through EEG signals using deep convolutional neural network. Med Biol Eng Compu 59(5):1167–1183
Urigüen JA, Garcia-Zapirain B (2015) EEG artifact removal, state-of-the-art and guidelines. J Neural Eng 12(3):1–23
Xia L, Malik AS, Subhani AR (2018) A physiological signal-based method for early mental-stress detection. Biomed Signal Process Control 1(46):18–32
Zhang Y, Zhang S, Ji X (2018) EEG-based classification of emotions using empirical mode decomposition and autoregressive model. Multim Tools and Appl 77(20):26697–26710
Zheng WL, Liu W, Lu Y, Lu BL, Cichocki A (2018) Emotionmeter: a multimodal framework for recognizing human emotions. IEEE Transactions on Cybernetics 49(3):1110–1122
Zou Y, Nathan V, Jafari R (2014) Automatic identification of artifact-related independent components for artifact removal in EEG recordings. IEEE J Biomed Health Inform 20(1):73–81
Acknowledgements
The authors are indebted to the editor and anonymous reviewers for their helpful comments and suggestions. The authors would like to thank GIK Institute for providing research facilities. This work was supported by the GIK Institute graduate program research fund under GA-4 scheme.
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Communicated by Tiancheng Yang.
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Rahman, A.U., Tubaishat, A., Al-Obeidat, F. et al. Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals. Soft Comput 26, 10687–10698 (2022). https://doi.org/10.1007/s00500-022-06847-w
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DOI: https://doi.org/10.1007/s00500-022-06847-w