EEG Sinyallerini İşlemek İçin Makine Öğreniminin Kullanıldığı Konular Üzerine Bir İnceleme

Year 2022, , 124 - 137, 30.06.2022

Shams Qahtan Omar Omar , Cengiz Tepe

https://doi.org/10.55117/bufbd.1099025

Cited By: 1

Abstract

Son on yılda, yapay zekâ (YZ) ve makine öğrenimi (MÖ) kullanımlarında bir artış görülmüştür. MÖ alanındaki son gelişmeler, farklı alanlar için elektroensefalografinin (EEG) kullanımına yeniden ilgi duyulmasına yol açmıştır. EEG, zihinsel iş yükünü ve yorgunluğu analiz etmek, beyin tümörlerini teşhis etmek ve merkezi sinir sistemi bozukluklarının rehabilitasyonu gibi tıbbi ve biyomedikal uygulamalarda; EEG tabanlı hareket analizi ve sınıflandırması ise, klinik uygulamalardan beyin-makine ara yüzüne ve robotik uygulamalara kadar birçok alanda yaygın olarak kullanılmaktadır. Bu makale, EEG sinyal işlemede kullanılan birçok MÖ algoritmalarının uygulamalarını gözden geçirmekte, yaygın olarak kullanılan algoritmaları, tipik uygulama senaryolarını, önemli ilerlemeleri ve mevcut sorunları tanıtmaktadır. Çalışmada, beyin-bilgisayar arayüzleri, bilişsel sinirbilim, beyin bozukluklarının teşhisi ve daha farklı konular dahil olmak üzere, EEG'deki mevcut MÖ uygulamaları araştırılmıştır. İlk olarak, evrişimli sinir ağı, destek vektör makineleri, K-en yakın komşu ve çok yönlü evrişim sinir ağı dahil olmak üzere EEG sinyal işlemede kullanılan MÖ algoritmalarının temel ilkeleri kısaca açıklanmıştır. Ayrıca EEG analizinde kullanılan MÖ uygulamalarına dair genel bir araştırma sunulmuştur. Sonuç olarak çalışmalarda en fazla DVM ve CNN yöntemlerinin kullanıldığı, çalışma başlıklarının ise ağırlıklı olarak epilepsi, BCI ve Duygu konularında en az ise Alkol, Uyku Durumları, Algı konularında yapıldığı belirlenmiştir.

Keywords

Elektroensefalografi, Makine öğrenme, Sinyal işleme, Özellik çıkarma, EEG

Supporting Institution

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Project Number

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Thanks

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A Review on Topics where Machine Learning has been used to Process EEG Signals

Abstract

The last decade has seen an increase in the use of artificial intelligence (AI) and machine learning (ML). Recent advances in the field of BC have led to renewed interest in the use of electroencephalography (EEG) for different fields. EEG is used in medical and biomedical applications such as analyzing mental workload and fatigue, diagnosing brain tumors and rehabilitation of central nervous system disorders; EEG-based motion analysis and classification is widely used in many areas from clinical applications to brain-machine interface and robotic applications. This article reviews the applications of many ML algorithms used in EEG signal processing, introduces commonly used algorithms, typical application scenarios, important advances and current problems. The study explored current applications of ML in EEG, including brain-computer interfaces, cognitive neuroscience, diagnosis of brain disorders, and more. First, the basic principles of ML algorithms used in EEG signal processing, including convolutional neural network, support vector machines, K-nearest neighbor and multidirectional convolutional neural network, are briefly explained. In addition, a general research on ML applications used in EEG analysis is presented. As a result, it was determined that the most SVM and CNN methods were used in the studies, and the study titles were mainly on epilepsy, BCI and Emotion, and the least on Alcohol, Sleeping States, Perception.

Keywords

Electroencephalography, Machine learning, Signal processing, Feature extraction, EEG

Project Number

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References

• [1] C. Lin et al., "Wireless and Wearable EEG System for Evaluating Driver Vigilance," IEEE Transactions on Biomedical Circuits and Systems, vol. 8, no. 2, pp. 165-176, 2014, doi: 10.1109/TBCAS.2014.2316224.
• [2] G. Zhang, V. Davoodnia, A. Sepas-Moghaddam, Y. Zhang, and A. Etemad, "Classification of Hand Movements From EEG Using a Deep Attention-Based LSTM Network," IEEE Sensors Journal, vol. 20, no. 6, pp. 3113-3122, 2020, doi: 10.1109/JSEN.2019.2956998.
• [3] L. Santos-Mayo, L. M. San-José-Revuelta, and J. I. Arribas, "A Computer-Aided Diagnosis System With EEG Based on the P3b Wave During an Auditory Odd-Ball Task in Schizophrenia," IEEE Transactions on Biomedical Engineering, vol. 64, no. 2, pp. 395-407, 2017, doi: 10.1109/TBME.2016.2558824.
• [4] J. Cao et al., "Unsupervised Eye Blink Artifact Detection From EEG With Gaussian Mixture Model," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 8, pp. 2895-2905, 2021, doi: 10.1109/JBHI.2021.3057891.
• [5] T. Wilaiprasitporn, A. Ditthapron, K. Matchaparn, T. Tongbuasirilai, N. Banluesombatkul, and E. Chuangsuwanich, "Affective EEG-Based Person Identification Using the Deep Learning Approach," IEEE Transactions on Cognitive and Developmental Systems, vol. 12, no. 3, pp. 486-496, 2020, doi: 10.1109/TCDS.2019.2924648.
• [6] T. Jeong, "Time-Series Data Classification and Analysis Associated With Machine Learning Algorithms for Cognitive Perception and Phenomenon," IEEE Access, vol. 8, pp. 222417-222428, 2020, doi: 10.1109/ACCESS.2020.3018477.
• [7] J. Yoo, L. Yan, D. El-Damak, M. A. B. Altaf, A. H. Shoeb, and A. P. Chandrakasan, "An 8-Channel Scalable EEG Acquisition SoC With Patient-Specific Seizure Classification and Recording Processor," IEEE Journal of Solid-State Circuits, vol. 48, no. 1, pp. 214-228, 2013, doi: 10.1109/JSSC.2012.2221220.
• [8] D. Pascual, A. Amirshahi, A. Aminifar, D. Atienza, P. Ryvlin, and R. Wattenhofer, "EpilepsyGAN: Synthetic Epileptic Brain Activities With Privacy Preservation," IEEE Transactions on Biomedical Engineering, vol. 68, no. 8, pp. 2435-2446, 2021, doi: 10.1109/TBME.2020.3042574.
• [9] W. Zheng, J. Zhu, and B. Lu, "Identifying Stable Patterns over Time for Emotion Recognition from EEG," IEEE Transactions on Affective Computing, vol. 10, no. 3, pp. 417-429, 2019, doi: 10.1109/TAFFC.2017.2712143.
• [10] S. K. Khare and V. Bajaj, "Time–Frequency Representation and Convolutional Neural Network-Based Emotion Recognition," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 2901-2909, 2021, doi: 10.1109/TNNLS.2020.3008938.
• [11] H. Cecotti and A. Graser, "Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 3, pp. 433-445, 2011, doi: 10.1109/TPAMI.2010.125.
• [12] B. Abibullaev and A. Zollanvari, "A Systematic Deep Learning Model Selection for P300-Based Brain-Computer Interfaces," IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-13, 2021, doi: 10.1109/TSMC.2021.3051136.
• [13] A. Supratak, H. Dong, C. Wu, and Y. Guo, "DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 11, pp. 1998-2008, 2017, doi: 10.1109/TNSRE.2017.2721116.
• [14] N. Banluesombatkul et al., "MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-Signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 6, pp. 1949-1963, 2021, doi: 10.1109/JBHI.2020.3037693.
• [15] B. Sun, X. Zhao, H. Zhang, R. Bai, and T. Li, "EEG Motor Imagery Classification With Sparse Spectrotemporal Decomposition and Deep Learning," IEEE Transactions on Automation Science and Engineering, vol. 18, no. 2, pp. 541-551, 2021, doi: 10.1109/TASE.2020.3021456.
• [16] N. Lu, T. Li, X. Ren, and H. Miao, "A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 6, pp. 566-576, 2017, doi: 10.1109/TNSRE.2016.2601240.
• [17] P. Zhang, X. Wang, W. Zhang, and J. Chen, "Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 1, pp. 31-42, 2019, doi: 10.1109/TNSRE.2018.2884641.
• [18] I. Kakkos et al., "EEG Fingerprints of Task-Independent Mental Workload Discrimination," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 10, pp. 3824-3833, 2021, doi: 10.1109/JBHI.2021.3085131.
• [19] D. Klepl, F. He, M. Wu, M. D. Marco, D. Blackburn, and P. G. Sarrigiannis, "Characterising Alzheimer’s Disease with EEG-based Energy Landscape Analysis," IEEE Journal of Biomedical and Health Informatics, pp. 1-1, 2021, doi: 10.1109/JBHI.2021.3105397.
• [20] P. Durongbhan et al., "A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 5, pp. 826-835, 2019, doi: 10.1109/TNSRE.2019.2909100.
• [21] G. Zhang and A. Etemad, "Capsule Attention for Multimodal EEG-EOG Representation Learning With Application to Driver Vigilance Estimation," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1138-1149, 2021, doi: 10.1109/TNSRE.2021.3089594.
• [22] Q. Chang et al., "Classification of First-Episode Schizophrenia, Chronic Schizophrenia and Healthy Control Based on Brain Network of Mismatch Negativity by Graph Neural Network," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1784-1794, 2021, doi: 10.1109/TNSRE.2021.3105669.
• [23] K. T. Kim, H. I. Suk, and S. W. Lee, "Commanding a Brain-Controlled Wheelchair Using Steady-State Somatosensory Evoked Potentials," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 3, pp. 654-665, 2018, doi: 10.1109/TNSRE.2016.2597854.
• [24] J. H. Jeong, K. H. Shim, D. J. Kim, and S. W. Lee, "Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 5, pp. 1226-1238, 2020, doi: 10.1109/TNSRE.2020.2981659.
• [25] V. Delvigne, H. Wannous, T. Dutoit, L. Ris, and J. P. Vandeborre, "PhyDAA: Physiological Dataset Assessing Attention," IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1, 2021, doi: 10.1109/TCSVT.2021.3061719.
• [26] L. Farsi, S. Siuly, E. Kabir, and H. Wang, "Classification of Alcoholic EEG Signals Using a Deep Learning Method," IEEE Sensors Journal, vol. 21, no. 3, pp. 3552-3560, 2021, doi: 10.1109/JSEN.2020.3026830.
• [27] A. Anuragi, D. S. Sisodia, and R. B. Pachori, "Automated Alcoholism Detection Using Fourier-Bessel Series Expansion Based Empirical Wavelet Transform," IEEE Sensors Journal, vol. 20, no. 9, pp. 4914-4924, 2020, doi: 10.1109/JSEN.2020.2966766.
• [28] S. R. Sreeja, R. R. Sahay, D. Samanta, and P. Mitra, "Removal of Eye Blink Artifacts From EEG Signals Using Sparsity," IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 5, pp. 1362-1372, 2018, doi: 10.1109/JBHI.2017.2771783.
• [29] S. Scholler et al., "Toward a Direct Measure of Video Quality Perception Using EEG," IEEE Transactions on Image Processing, vol. 21, no. 5, pp. 2619-2629, 2012, doi: 10.1109/TIP.2012.2187672.
• [30] D. Novak, X. Omlin, R. Leins-Hess, and R. Riener, "Predicting Targets of Human Reaching Motions Using Different Sensing Technologies," IEEE Transactions on Biomedical Engineering, vol. 60, no. 9, pp. 2645-2654, 2013, doi: 10.1109/TBME.2013.2262455.
• [31] O. Avilov, S. Rimbert, A. Popov, and L. Bougrain, "Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness," IEEE Transactions on Biomedical Engineering, vol. 68, no. 10, pp. 3087-3097, 2021, doi: 10.1109/TBME.2021.3064794.
• [32] T. Tazrin, Q. A. Rahman, M. M. Fouda, and Z. M. Fadlullah, "LiHEA: Migrating EEG Analytics to Ultra-Edge IoT Devices With Logic-in-Headbands," IEEE Access, vol. 9, pp. 138834-138848, 2021, doi: 10.1109/ACCESS.2021.3118971.
• [33] A. O. Akmandor, Y. I. N. H, and N. K. Jha, "Smart, Secure, Yet Energy-Efficient, Internet-of-Things Sensors," IEEE Transactions on Multi-Scale Computing Systems, vol. 4, no. 4, pp. 914-930, 2018, doi: 10.1109/TMSCS.2018.2864297.
• [34] X. Jin et al., "CTNN: A Convolutional Tensor-Train Neural Network for Multi-Task Brainprint Recognition," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 103-112, 2021, doi: 10.1109/TNSRE.2020.3035786.
• [35] N. Vivaldi, M. Caiola, K. Solarana, and M. Ye, "Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification," IEEE Transactions on Biomedical Engineering, vol. 68, no. 11, pp. 3205-3216, 2021, doi: 10.1109/TBME.2021.3062502.
• [36] I. Hussain and S. J. Park, "HealthSOS: Real-Time Health Monitoring System for Stroke Prognostics," IEEE Access, vol. 8, pp. 213574-213586, 2020, doi: 10.1109/ACCESS.2020.3040437.