Research Article
BibTex RIS Cite

Data Acquisition with Mobile EEG Devices: A Methodological Guide and Evaluation with Applications

Year 2025, Volume: 15 Issue: 2, 897 - 922, 15.06.2025

Çağatay Murat Yılmaz , Bahar Hatipoğlu Yılmaz

https://doi.org/10.31466/kfbd.1625810

Abstract

EEG (Electroencephalography) is an important technology that enables non-invasive measurement of brain activity and is used in various fields such as diagnosis and treatment of neurological disorders and brain-computer interfaces. Mobile EEG devices can record signals of similar quality to clinical counterparts. Eliminating the dependence on the fixed and controlled recording environments required by clinic EEG systems enables real-time monitoring and analysis of brain signals under real-life scenarios, even on the move. This paper addresses the lack of experience in the mobile EEG recording process literature. By experimentally demonstrating the recording process, this study aims to enhance methodological reliability for future research, particularly regarding data acquisition. Within the scope of the study, the steps regarding the recording and preprocessing EEG signals are described in technical detail, and experimental evaluations are made on the datasets generated for motor imagery and emotion recognition tasks. The findings demonstrate that mobile EEG devices can be used reliably in research despite their technical limitations. The methodological framework presented here is a practical reference for researchers utilizing mobile EEG systems.

Keywords

EEG Mobile devices Motor imagery Emotion recognition Brain-computer interface

References

  • Acharya, D., Billimoria, A., Srivastava, N., Goel, S., Bhardwaj, A. (2020). Emotion recognition using fourier transform and genetic programming. Applied Acoustics, 164, 107260. https://doi.org/10.1016/j.apacoust.2020.107260
  • Al-Hamadani, A. A., Mohammed, M. J., Tariq, S. M. (2023). Normalized deep learning algorithms based information aggregation functions to classify motor imagery EEG signal. Neural Computing and Applications, 35(30), 22725-22736. https://doi.org/10.1007/s00521-023-08944-9
  • Al-Saegh, A., Dawwd, S.A., Abdul-Jabbar, J.M. (2021). CutCat: An augmentation method for EEG classification. Neural Networks, 141: 433-443. https://doi.org/10.1016/j.neunet.2021.05.032
  • Arad, E., Bartsch, R. P., Kantelhardt, J. W., Plotnik, M. (2018). Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion. PloS one, 13(5), e0197153.https://doi.org/10.1371/journal.pone.0197153
  • Arvaneh, M., Guan, C., Ang, K. K. et al. (2011). Optimizing the channel selection and classification accuracy in EEG-based BCI. IEEE Transactions on Biomedical Engineering, 58(6), 1865-1873. https://doi.org/10.1109/TBME.2011.2131142
  • Biondi, A., Santoro, V., Viana, P. F., Laiou, P., Pal, D. K., Bruno, E., Richardson, M. P. (2022). Noninvasive mobile EEG as a tool for seizure monitoring and management: A systematic review. Epilepsia, 63(5), 1041-1063. https://doi.org/10.1111/epi.17220
  • BioSemi Products. https://www.biosemi.com/research.htm (2025, Mart 25).
  • Boehner, K., DePaula, R., Dourish, P., Sengers, P. (2007). How emotion is made and measured. International Journal of Human-Computer Studies, 65(4), 275-291. https://doi.org/10.1016/j.ijhcs.2006.11.016
  • Brain Products Neurophysiology EEG Solutions. Available online: https://www.brainproducts.com/solutions/ (2025, Mart 25).
  • Brunner, C., Naeem, M., Leeb, R., Graimann, B., Pfurtscheller, G. (2007). Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis. Pattern Recognition Letters, 28(8): 957-964. https://doi.org/10.1016/j.patrec.2007.01.002
  • CGX EEG Headsets and Devices. https://www.cgxsystems.com/products (2025, Mart 25).
  • Cruz-Vazquez, J. A., Montiel-Pérez, J. Y., Romero-Herrera, R., Rubio-Espino, E. (2025). Emotion recognition from EEG signals using advanced transformations and deep learning. Mathematics, 13(2), 254. https://doi.org/10.3390/math13020254
  • Delorme, A. (2023). EEG is better left alone. Scientific reports, 13(1), 2372. https://doi.org/10.1038/s41598-023-27528-0
  • Delorme, A., Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9-21. https://doi.org/10.1016/j.jneumeth.2003.10.009
  • Doma, O.O. (2024). EEG as an Input for Virtual Reality. In: Lee, N. (eds) Encyclopedia of Computer Graphics and Games. Springer, Cham. https://doi.org/10.1007/978-3-031-23161-2\_176
  • EEGLAB. https://sccn.ucsd.edu/eeglab/index.php (2025, Nisan 11).
  • EEGLAB Plugins. https://sccn.ucsd.edu/eeglab/plugin_uploader/plugin_list_all.php (2025, Nisan 11).
  • Emotiv, Emotiv Brain Data Measuring Hardware and Software Solutions. https://www.emotiv.com/ (2025, Mart 25).
  • EmotivPRO. https://emotiv.gitbook.io/system-requirements/emotivpro-v3.0 (2025, Ocak 02).
  • EPOC Flex Cap. https://www.emotiv.com/products/epoc-flex-cap (2025, Ocak 02).
  • EPOC Flex User Manual. https://emotiv.gitbook.io/epoc-flex-user-manual (2025, Ocak 02).
  • Fakhruzzaman, M. N., Riksakomara, E., Suryotrisongko, H. (2015). EEG wave identification in human brain with Emotiv EPOC for motor imagery. Procedia Computer Science, 72, 269-276. https://doi.org/10.1016/j.procs.2015.12.140
  • Flex 2 Saline - 32 Channel Wireless EEG Head Cap System. https://www.emotiv.com/products/flex-saline (2025, Ocak 02).
  • Guger, C., Krausz, G., Allison, B. Z., Edlinger, G. (2012). Comparison of dry and gel based electrodes for P300 brain–computer interfaces. Frontiers in neuroscience, 6, 60. https://doi.org/10.3389/fnins.2012.00060
  • Hatipoğlu Yılmaz, B., (2022). Sinyal Görüntü Dönüşümleri Yardımıyla Çok Modlu Duygu Tanıma Sisteminin Geliştirilmesi. Doktora Tezi, Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Trabzon.
  • Hatipoglu Yilmaz, B., Kose, C., Yilmaz, C. M. A novel multimodal EEG-image fusion approach for emotion recognition: introducing a multimodal KMED dataset. Neural Comput & Applic 37, 5187–5202 (2025). https://doi.org/10.1007/s00521-024-10925-5
  • He Z, Li Z, Yang F, Wang L, Li J, Zhou C, Pan J. Advances in Multimodal Emotion Recognition Based on Brain-Computer Interfaces. Brain Sciences. 2020 Sep 29;10(10):687. https://doi.org/10.3390/brainsci10100687
  • Hu, S., Lai, Y., Valdes-Sosa, P. A., Bringas-Vega, M. L., Yao, D. (2018). How do reference montage and electrodes setup affect the measured scalp EEG potentials?. Journal of neural engineering, 15(2), https://www.doi.org/10.1088/1741-2552/aaa13f
  • Huang, G., Zhao, Z., Zhang, S. et al. (2023). Discrepancy between inter-and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives. Frontiers in neuroscience, 17, 1122661. https://doi.org/10.3389/fnins.2023.1122661
  • Jain, A., Raja, R., Srivastava, S. et al. (2024). Analysis of EEG signals and data acquisition methods: a review. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 12 (1), 2304574. https://doi.org/10.1080/21681163.2024.2304574
  • Jiang, X., Bian, G. B., Tian, Z. (2019). Removal of artifacts from EEG signals: a review. Sensors, 19(5), 987. https://doi.org/10.3390/s19050987
  • Kancaoğlu, M., Kuntalp, M. (2024). Low-cost, mobile EEG hardware for SSVEP applications. HardwareX, 19, e00567. https://doi.org/10.1016/j.ohx.2024.e00567
  • Kaveh, R., Schwendeman, C., Pu, L. et al. Wireless ear EEG to monitor drowsiness. Nat Commun 15, 6520 (2024). https://doi.org/10.1038/s41467-024-48682-7
  • Kothe, C., The Artifact Subspace Reconstruction Method, https://sccn.ucsd.edu/githubwiki/files/asr-final-export.pdf (2025, Nisan 24).
  • Kucukler, O. F., Amira, A., Malekmohamadi, H. (2024). EEG channel selection using Gramian Angular Fields and spectrograms for energy data visualization. Engineering Applications of Artificial Intelligence, 133, 108305.
  • Kutafina, E., Brenner, A., Titgemeyer, Y., Surges, R., Jonas, S. (2020). Comparison of mobile and clinical EEG sensors through resting state simultaneous data collection. PeerJ, 8, e8969. https://doi.org/10.7717/peerj.8969
  • Li GL, Wu JT, Xia YH, He QG, Jin HG. Review of semi-dry electrodes for EEG recording. J Neural Eng. 2020 Oct 23;17(5):051004. doi: 10.1088/1741-2552/abbd50.
  • Li, W., Ma, Y., Shao, K., Yi, Z. et. al. "The Human–Machine Interface Design Based on sEMG and Motor Imagery EEG for Lower Limb Exoskeleton Assistance System," in IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-14, 2024, Art no. 6502914, doi: 10.1109/TIM.2024.3375980
  • Li, Y., Wang, P. T., Vaidya, M. P., Flint, R. D., Liu, C. Y., Slutzky, M. W., Do, A. H. (2021). Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE)—A novel ICA-based algorithm for removing myoelectric artifacts from EEG. Frontiers in neuroscience, 14, 597941. https://doi.org/10.3389/fnins.2020.597941
  • Mullen, T. R., Kothe, C. A., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., Cauwenberghs, G. (2015). Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE transactions on biomedical engineering, 62(11), 2553-2567. https://www.doi.org/10.1109/TBME.2015.2481482
  • Muse: the brain sensing headband. https://choosemuse.com/ (2025, Mart 25).
  • NeuroSky brainwave sensing headsets. https://store.neurosky.com/ (2025, Mart 25).
  • Newman, A. J., Neural Data Science, Averaging and Re-Referencing ERPs. https://neuraldatascience.io/7-eeg/erp_avg_reref.html (2025, Mart 25).
  • OpenBCI EEG Hardware and Software. https://openbci.com/ (2025, Mart 25).
  • OpenBCI-Datasets, Publicly Available EEG Datasets. https://openbci.com/community/publicly-available-eeg-datasets/ (2025, Nisan 04).
  • Paszkiel, S., Rojek, R., Lei, N., Castro, M.A. (2021) A Pilot Study of Game Design in the Unity Environment as an Example of the Use of Neurogaming on the Basis of Brain–Computer Interface Technology to Improve Concentration. NeuroSci, 2, 109-119. https://doi.org/10.3390/neurosci2020007
  • Permana, K., Wijaya, S. K., Prajitno, P. (2019, November). Controlled wheelchair based on brain computer interface using Neurosky Mindwave Mobile 2. In AIP Conference Proceedings (Vol. 2168, No. 1). AIP Publishing. https://doi.org/10.1063/1.5132449
  • Pion-Tonachini, L., Kreutz-Delgado, K., Makeig, S. (2019). ICLabel: An automated electroencephalographic independent component classifier, dataset, and website. NeuroImage, 198, 181-197. https://doi.org/10.1016/j.neuroimage.2019.05.026
  • Saichoo, T., Boonbrahm, P., Punsawad, Y. (2022). Investigating user proficiency of motor imagery for EEG-based BCI system to control simulated wheelchair. Sensors, 22(24), 9788. https://doi.org/10.3390/s22249788
  • Sartipi, S. ve Cetin, M., "Subject-Independent Deep Architecture for EEG-Based Motor Imagery Classification," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 718-727, 2024. https://www.doi.org/10.1109/TNSRE.2024.3360194
  • Shah AK, Mittal S. Invasive electroencephalography monitoring: Indications and presurgical planning. Ann Indian Acad Neurol. 2014 Mar;17(Suppl 1):S89-94. https://www.doi.org/10.4103/0972-2327.128668
  • Shi, T. W., Chang, G. M., Qiang, J. F., Ren, L., Cui, W. H. (2023). Brain computer interface system based on monocular vision and motor imagery for UAV indoor space target searching. Biomedical Signal Processing and Control, 79, 104114.
  • Soufineyestani, M.; Dowling, D.; Khan, A. Electroencephalography (EEG) Technology Applications and Available Devices. Appl. Sci. 2020, 10, 7453. https://doi.org/10.3390/app10217453
  • Taherysayah, F., Malathouni, C., Liang, H. N., Westermann, C. (2024). Virtual reality and electroencephalography in architectural design: A systematic review of empirical studies. Journal of Building Engineering, 108611. https://doi.org/10.1016/j.jobe.2024.108611
  • Titgemeyer, Y., Surges, R., Altenmüller, D. M., Fauser, S., Kunze, A., Lanz, M. et al. (2020). Can commercially available wearable EEG devices be used for diagnostic purposes? An explorative pilot study. Epilepsy & Behavior, 103, 106507. https://doi.org/10.1016/j.yebeh.2019.106507
  • Tülay, E. E., Balli, T. (2024). Decoding functional brain data for emotion recognition: a machine learning approach. ACM Transactions on Applied Perception, 21(3), 1-18. https://doi.org/10.1145/3657638
  • Vazquez, J. A. C., Pérez, J. Y. M., Herrera, R. R. (2024, June). Emotion Recognition Through EEG Signals With Movie Clips Stimuli. In 2024 4th Interdisciplinary Conference on Electrics and Computer (INTCEC) (pp. 1-6). IEEE. https://www.doi.org/10.1109/INTCEC61833.2024.10602937
  • Wang, Y., Zibrandtsen, I. C., Lazeron, R. H. et al. (2023). Pitfalls in EEG analysis in patients with nonconvulsive status epilepticus: A preliminary study. Clinical EEG and Neuroscience, 54(3), 255-264. https://doi.org/10.1177/15500594211050492
  • Weiergräber, M., Papazoglou, A., Broich, K., Müller, R. (2016). Sampling rate, signal bandwidth and related pitfalls in EEG analysis. Journal of neuroscience methods, 268, 53-55. https://doi.org/10.1016/j.jneumeth.2016.05.010
  • Why Does Emotiv EPOC+ Stop At 43 Hz In Range? Is There A Way To Expand This Somehow? https://www.emotiv.com/tools/knowledge-base/faq/emotiv-epoc-stop-at-43-hz-in-range (2025, Ocak 02).
  • William Tatum, D. O., Feyissa, A. M. et al., Technical Aspects of EEG. https://www.medlink.com/articles/technical-aspects-of-eeg (2025, Mart 26).
  • Williams, N. S., McArthur, G. M., de Wit, B., Ibrahim, G., Badcock, N. A. (2020). A validation of Emotiv EPOC Flex saline for EEG and ERP research. PeerJ, 8, e9713. https://www.doi.org/10.7717/peerj.9713
  • Yang, K., Tong, L., Shu, J., Zhuang, N., Yan, B., Zeng, Y. (2020). High gamma band EEG closely related to emotion: evidence from functional network. Frontiers in human neuroscience, 14, 89, https://www.doi.org/10.3389/fnhum.2020.00089
  • Yilmaz, C. M., (2021). Motor Hareket Hayali Görevlerine ait EEG Sinyallerinin 2-B Öznitelikler ve Yarı Olasılıksal Dağılım Modelleri ile Sınıflandırılması. Doktora Tezi, Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Trabzon.
  • Yilmaz, C. M., Yilmaz, B. H., Kose, C. MI-BMPI motor imagery brain–mobile phone dataset and performance evaluation of voting ensembles utilizing QPDM. Neural Comput & Applic 37, 4679–4696 (2025). https://doi.org/10.1007/s00521-024-10917-5
  • Yuan, H., Li, Y., Yang, J. et al. (2021). State of the art of non-invasive electrode materials for brain–computer interface. Micromachines, 12(12), 1521. https://doi.org/10.3390/mi12121521
  • Zhang, X., Wang, Y., Tang, Y. et al. Adaptive filter of frequency bands based coordinate attention network for EEG-based motor imagery classification. Health Inf Sci Syst 12, 11 (2024). https://doi.org/10.1007/s13755-024-00270-1
  • Zhao, G., Zhang, Y., Ge, Y. (2018). Frontal EEG asymmetry and middle line power difference in discrete emotions. Frontiers in behavioral neuroscience, 12, 225, https://www.doi.org/10.3389/fnbeh.2018.00225
  • Zhao K, Zhang Z, Wen H, Scano A. Intra-Subject and Inter-Subject Movement Variability Quantified with Muscle Synergies in Upper-Limb Reaching Movements. Biomimetics (Basel). 2021 Oct 20;6(4):63. doi: 10.3390/biomimetics6040063. PMID: 34698082; PMCID: PMC8544238.
  • Zheng WL, Liu W, Lu Y, Lu BL, Cichocki A. EmotionMeter: A Multimodal Framework for Recognizing Human Emotions. IEEE Trans Cybern. 2019 49 (3), 1110-1122, https://www.doi.org/10.1109/TCYB.2018.2797176
  • Zheng, W. L., Lu, B. L. (2015). Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Transactions on autonomous mental development, 7(3), 162-175., https://www.doi.org/10.1109/TAMD.2015.2431497

Mobil EEG Cihazları ile Veri Toplama: Metodolojik Bir Kılavuz ve Uygulamalarla Değerlendirme

Year 2025, Volume: 15 Issue: 2, 897 - 922, 15.06.2025

Çağatay Murat Yılmaz , Bahar Hatipoğlu Yılmaz

https://doi.org/10.31466/kfbd.1625810

Abstract

EEG (Elektroensefalografi), beyin aktivitelerinin invaziv olmayan şekilde ölçülmesini sağlayan ve nörolojik bozuklukların teşhis ve tedavisinde ile beyin-bilgisayar arayüzleri gibi çeşitli alanlarda kullanılan önemli bir teknolojidir. Mobil EEG kayıt cihazları, klinik alternatiflerine benzer kalitede sinyaller kaydedebilmektedir. Geleneksel EEG sistemlerinin gerektirdiği sabit ve kontrollü kayıt ortamlarına olan bağımlılığı ortadan kaldırarak, gerçek yaşam koşullarında, hatta hareket halindeyken bile beyin sinyallerinin gerçek zamanlı izlenmesini ve analizini mümkün kılmaktadır. Bu çalışma, mobil EEG ile kayıt alma sürecine dair literatürdeki deneyim eksikliğini gidermeyi amaçlamaktadır. Kayıt sürecinin deneysel aktarımıyla, benzer araştırmalarda özellikle kayıt alma sürecinde yöntemsel güvenilirliğin artırılması hedeflenmiştir. Çalışma kapsamında, EEG sinyallerinin kaydedilmesi ve ön işlenmesine ilişkin adımlar teknik detaylarla aktarılmış; motor hareket hayali ve duygu tanıma görevleri için oluşturulan veri setleri kullanılarak deneysel değerlendirmeler yapılmıştır. Mobil EEG cihazlarının teknik farklılıklarına rağmen, araştırmalarda güvenilir biçimde kullanılabileceği ortaya konmuştur. Sunulan yöntemsel kılavuz, mobil EEG sistemleriyle çalışacak araştırmacılar için pratik bir başvuru kaynağı niteliği taşımaktadır.

Keywords

EEG Mobil cihazlar Motor hareket hayali Duygu tanıma Beyin-bilgisayar arayüzü

Ethical Statement

Yapılan çalışmada araştırma ve yayın etiğine uyulmuştur

Supporting Institution

TÜBİTAK

Thanks

Bu çalışma 119E397 nolu “EEG Temelli Beyin Bilgisayar Arayüzü Sistemlerine ait Motor Hareket Hayali Görevlerinin Sınıflandırılmasına Yönelik Örüntü Tanıma Yöntemlerin Geliştirilmesi” ve başlıklı TÜBİTAK 1002 Hızlı Destek Programı projesi tarafından desteklenerek edinilen EEG cihazı ve ilişkili çalışmaların katkısıyla gerçekleştirilmiştir. Duygu tanıma çalışmaları ise 121E002 nolu “Sinyal Görüntü Dönüşümü Yöntemi Yardımıyla Çok Yönlü Duygu Tanıma Sisteminin Geliştirilmesi” başlıklı TÜBİTAK 1002 Hızlı Destek Programı projesi kapsamında tamamlanmıştır. Verdikleri destekler için TÜBİTAK'a teşekkür ederiz.

References

  • Acharya, D., Billimoria, A., Srivastava, N., Goel, S., Bhardwaj, A. (2020). Emotion recognition using fourier transform and genetic programming. Applied Acoustics, 164, 107260. https://doi.org/10.1016/j.apacoust.2020.107260
  • Al-Hamadani, A. A., Mohammed, M. J., Tariq, S. M. (2023). Normalized deep learning algorithms based information aggregation functions to classify motor imagery EEG signal. Neural Computing and Applications, 35(30), 22725-22736. https://doi.org/10.1007/s00521-023-08944-9
  • Al-Saegh, A., Dawwd, S.A., Abdul-Jabbar, J.M. (2021). CutCat: An augmentation method for EEG classification. Neural Networks, 141: 433-443. https://doi.org/10.1016/j.neunet.2021.05.032
  • Arad, E., Bartsch, R. P., Kantelhardt, J. W., Plotnik, M. (2018). Performance-based approach for movement artifact removal from electroencephalographic data recorded during locomotion. PloS one, 13(5), e0197153.https://doi.org/10.1371/journal.pone.0197153
  • Arvaneh, M., Guan, C., Ang, K. K. et al. (2011). Optimizing the channel selection and classification accuracy in EEG-based BCI. IEEE Transactions on Biomedical Engineering, 58(6), 1865-1873. https://doi.org/10.1109/TBME.2011.2131142
  • Biondi, A., Santoro, V., Viana, P. F., Laiou, P., Pal, D. K., Bruno, E., Richardson, M. P. (2022). Noninvasive mobile EEG as a tool for seizure monitoring and management: A systematic review. Epilepsia, 63(5), 1041-1063. https://doi.org/10.1111/epi.17220
  • BioSemi Products. https://www.biosemi.com/research.htm (2025, Mart 25).
  • Boehner, K., DePaula, R., Dourish, P., Sengers, P. (2007). How emotion is made and measured. International Journal of Human-Computer Studies, 65(4), 275-291. https://doi.org/10.1016/j.ijhcs.2006.11.016
  • Brain Products Neurophysiology EEG Solutions. Available online: https://www.brainproducts.com/solutions/ (2025, Mart 25).
  • Brunner, C., Naeem, M., Leeb, R., Graimann, B., Pfurtscheller, G. (2007). Spatial filtering and selection of optimized components in four class motor imagery EEG data using independent components analysis. Pattern Recognition Letters, 28(8): 957-964. https://doi.org/10.1016/j.patrec.2007.01.002
  • CGX EEG Headsets and Devices. https://www.cgxsystems.com/products (2025, Mart 25).
  • Cruz-Vazquez, J. A., Montiel-Pérez, J. Y., Romero-Herrera, R., Rubio-Espino, E. (2025). Emotion recognition from EEG signals using advanced transformations and deep learning. Mathematics, 13(2), 254. https://doi.org/10.3390/math13020254
  • Delorme, A. (2023). EEG is better left alone. Scientific reports, 13(1), 2372. https://doi.org/10.1038/s41598-023-27528-0
  • Delorme, A., Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), 9-21. https://doi.org/10.1016/j.jneumeth.2003.10.009
  • Doma, O.O. (2024). EEG as an Input for Virtual Reality. In: Lee, N. (eds) Encyclopedia of Computer Graphics and Games. Springer, Cham. https://doi.org/10.1007/978-3-031-23161-2\_176
  • EEGLAB. https://sccn.ucsd.edu/eeglab/index.php (2025, Nisan 11).
  • EEGLAB Plugins. https://sccn.ucsd.edu/eeglab/plugin_uploader/plugin_list_all.php (2025, Nisan 11).
  • Emotiv, Emotiv Brain Data Measuring Hardware and Software Solutions. https://www.emotiv.com/ (2025, Mart 25).
  • EmotivPRO. https://emotiv.gitbook.io/system-requirements/emotivpro-v3.0 (2025, Ocak 02).
  • EPOC Flex Cap. https://www.emotiv.com/products/epoc-flex-cap (2025, Ocak 02).
  • EPOC Flex User Manual. https://emotiv.gitbook.io/epoc-flex-user-manual (2025, Ocak 02).
  • Fakhruzzaman, M. N., Riksakomara, E., Suryotrisongko, H. (2015). EEG wave identification in human brain with Emotiv EPOC for motor imagery. Procedia Computer Science, 72, 269-276. https://doi.org/10.1016/j.procs.2015.12.140
  • Flex 2 Saline - 32 Channel Wireless EEG Head Cap System. https://www.emotiv.com/products/flex-saline (2025, Ocak 02).
  • Guger, C., Krausz, G., Allison, B. Z., Edlinger, G. (2012). Comparison of dry and gel based electrodes for P300 brain–computer interfaces. Frontiers in neuroscience, 6, 60. https://doi.org/10.3389/fnins.2012.00060
  • Hatipoğlu Yılmaz, B., (2022). Sinyal Görüntü Dönüşümleri Yardımıyla Çok Modlu Duygu Tanıma Sisteminin Geliştirilmesi. Doktora Tezi, Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Trabzon.
  • Hatipoglu Yilmaz, B., Kose, C., Yilmaz, C. M. A novel multimodal EEG-image fusion approach for emotion recognition: introducing a multimodal KMED dataset. Neural Comput & Applic 37, 5187–5202 (2025). https://doi.org/10.1007/s00521-024-10925-5
  • He Z, Li Z, Yang F, Wang L, Li J, Zhou C, Pan J. Advances in Multimodal Emotion Recognition Based on Brain-Computer Interfaces. Brain Sciences. 2020 Sep 29;10(10):687. https://doi.org/10.3390/brainsci10100687
  • Hu, S., Lai, Y., Valdes-Sosa, P. A., Bringas-Vega, M. L., Yao, D. (2018). How do reference montage and electrodes setup affect the measured scalp EEG potentials?. Journal of neural engineering, 15(2), https://www.doi.org/10.1088/1741-2552/aaa13f
  • Huang, G., Zhao, Z., Zhang, S. et al. (2023). Discrepancy between inter-and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives. Frontiers in neuroscience, 17, 1122661. https://doi.org/10.3389/fnins.2023.1122661
  • Jain, A., Raja, R., Srivastava, S. et al. (2024). Analysis of EEG signals and data acquisition methods: a review. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 12 (1), 2304574. https://doi.org/10.1080/21681163.2024.2304574
  • Jiang, X., Bian, G. B., Tian, Z. (2019). Removal of artifacts from EEG signals: a review. Sensors, 19(5), 987. https://doi.org/10.3390/s19050987
  • Kancaoğlu, M., Kuntalp, M. (2024). Low-cost, mobile EEG hardware for SSVEP applications. HardwareX, 19, e00567. https://doi.org/10.1016/j.ohx.2024.e00567
  • Kaveh, R., Schwendeman, C., Pu, L. et al. Wireless ear EEG to monitor drowsiness. Nat Commun 15, 6520 (2024). https://doi.org/10.1038/s41467-024-48682-7
  • Kothe, C., The Artifact Subspace Reconstruction Method, https://sccn.ucsd.edu/githubwiki/files/asr-final-export.pdf (2025, Nisan 24).
  • Kucukler, O. F., Amira, A., Malekmohamadi, H. (2024). EEG channel selection using Gramian Angular Fields and spectrograms for energy data visualization. Engineering Applications of Artificial Intelligence, 133, 108305.
  • Kutafina, E., Brenner, A., Titgemeyer, Y., Surges, R., Jonas, S. (2020). Comparison of mobile and clinical EEG sensors through resting state simultaneous data collection. PeerJ, 8, e8969. https://doi.org/10.7717/peerj.8969
  • Li GL, Wu JT, Xia YH, He QG, Jin HG. Review of semi-dry electrodes for EEG recording. J Neural Eng. 2020 Oct 23;17(5):051004. doi: 10.1088/1741-2552/abbd50.
  • Li, W., Ma, Y., Shao, K., Yi, Z. et. al. "The Human–Machine Interface Design Based on sEMG and Motor Imagery EEG for Lower Limb Exoskeleton Assistance System," in IEEE Transactions on Instrumentation and Measurement, vol. 73, pp. 1-14, 2024, Art no. 6502914, doi: 10.1109/TIM.2024.3375980
  • Li, Y., Wang, P. T., Vaidya, M. P., Flint, R. D., Liu, C. Y., Slutzky, M. W., Do, A. H. (2021). Electromyogram (EMG) Removal by Adding Sources of EMG (ERASE)—A novel ICA-based algorithm for removing myoelectric artifacts from EEG. Frontiers in neuroscience, 14, 597941. https://doi.org/10.3389/fnins.2020.597941
  • Mullen, T. R., Kothe, C. A., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., Cauwenberghs, G. (2015). Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE transactions on biomedical engineering, 62(11), 2553-2567. https://www.doi.org/10.1109/TBME.2015.2481482
  • Muse: the brain sensing headband. https://choosemuse.com/ (2025, Mart 25).
  • NeuroSky brainwave sensing headsets. https://store.neurosky.com/ (2025, Mart 25).
  • Newman, A. J., Neural Data Science, Averaging and Re-Referencing ERPs. https://neuraldatascience.io/7-eeg/erp_avg_reref.html (2025, Mart 25).
  • OpenBCI EEG Hardware and Software. https://openbci.com/ (2025, Mart 25).
  • OpenBCI-Datasets, Publicly Available EEG Datasets. https://openbci.com/community/publicly-available-eeg-datasets/ (2025, Nisan 04).
  • Paszkiel, S., Rojek, R., Lei, N., Castro, M.A. (2021) A Pilot Study of Game Design in the Unity Environment as an Example of the Use of Neurogaming on the Basis of Brain–Computer Interface Technology to Improve Concentration. NeuroSci, 2, 109-119. https://doi.org/10.3390/neurosci2020007
  • Permana, K., Wijaya, S. K., Prajitno, P. (2019, November). Controlled wheelchair based on brain computer interface using Neurosky Mindwave Mobile 2. In AIP Conference Proceedings (Vol. 2168, No. 1). AIP Publishing. https://doi.org/10.1063/1.5132449
  • Pion-Tonachini, L., Kreutz-Delgado, K., Makeig, S. (2019). ICLabel: An automated electroencephalographic independent component classifier, dataset, and website. NeuroImage, 198, 181-197. https://doi.org/10.1016/j.neuroimage.2019.05.026
  • Saichoo, T., Boonbrahm, P., Punsawad, Y. (2022). Investigating user proficiency of motor imagery for EEG-based BCI system to control simulated wheelchair. Sensors, 22(24), 9788. https://doi.org/10.3390/s22249788
  • Sartipi, S. ve Cetin, M., "Subject-Independent Deep Architecture for EEG-Based Motor Imagery Classification," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 718-727, 2024. https://www.doi.org/10.1109/TNSRE.2024.3360194
  • Shah AK, Mittal S. Invasive electroencephalography monitoring: Indications and presurgical planning. Ann Indian Acad Neurol. 2014 Mar;17(Suppl 1):S89-94. https://www.doi.org/10.4103/0972-2327.128668
  • Shi, T. W., Chang, G. M., Qiang, J. F., Ren, L., Cui, W. H. (2023). Brain computer interface system based on monocular vision and motor imagery for UAV indoor space target searching. Biomedical Signal Processing and Control, 79, 104114.
  • Soufineyestani, M.; Dowling, D.; Khan, A. Electroencephalography (EEG) Technology Applications and Available Devices. Appl. Sci. 2020, 10, 7453. https://doi.org/10.3390/app10217453
  • Taherysayah, F., Malathouni, C., Liang, H. N., Westermann, C. (2024). Virtual reality and electroencephalography in architectural design: A systematic review of empirical studies. Journal of Building Engineering, 108611. https://doi.org/10.1016/j.jobe.2024.108611
  • Titgemeyer, Y., Surges, R., Altenmüller, D. M., Fauser, S., Kunze, A., Lanz, M. et al. (2020). Can commercially available wearable EEG devices be used for diagnostic purposes? An explorative pilot study. Epilepsy & Behavior, 103, 106507. https://doi.org/10.1016/j.yebeh.2019.106507
  • Tülay, E. E., Balli, T. (2024). Decoding functional brain data for emotion recognition: a machine learning approach. ACM Transactions on Applied Perception, 21(3), 1-18. https://doi.org/10.1145/3657638
  • Vazquez, J. A. C., Pérez, J. Y. M., Herrera, R. R. (2024, June). Emotion Recognition Through EEG Signals With Movie Clips Stimuli. In 2024 4th Interdisciplinary Conference on Electrics and Computer (INTCEC) (pp. 1-6). IEEE. https://www.doi.org/10.1109/INTCEC61833.2024.10602937
  • Wang, Y., Zibrandtsen, I. C., Lazeron, R. H. et al. (2023). Pitfalls in EEG analysis in patients with nonconvulsive status epilepticus: A preliminary study. Clinical EEG and Neuroscience, 54(3), 255-264. https://doi.org/10.1177/15500594211050492
  • Weiergräber, M., Papazoglou, A., Broich, K., Müller, R. (2016). Sampling rate, signal bandwidth and related pitfalls in EEG analysis. Journal of neuroscience methods, 268, 53-55. https://doi.org/10.1016/j.jneumeth.2016.05.010
  • Why Does Emotiv EPOC+ Stop At 43 Hz In Range? Is There A Way To Expand This Somehow? https://www.emotiv.com/tools/knowledge-base/faq/emotiv-epoc-stop-at-43-hz-in-range (2025, Ocak 02).
  • William Tatum, D. O., Feyissa, A. M. et al., Technical Aspects of EEG. https://www.medlink.com/articles/technical-aspects-of-eeg (2025, Mart 26).
  • Williams, N. S., McArthur, G. M., de Wit, B., Ibrahim, G., Badcock, N. A. (2020). A validation of Emotiv EPOC Flex saline for EEG and ERP research. PeerJ, 8, e9713. https://www.doi.org/10.7717/peerj.9713
  • Yang, K., Tong, L., Shu, J., Zhuang, N., Yan, B., Zeng, Y. (2020). High gamma band EEG closely related to emotion: evidence from functional network. Frontiers in human neuroscience, 14, 89, https://www.doi.org/10.3389/fnhum.2020.00089
  • Yilmaz, C. M., (2021). Motor Hareket Hayali Görevlerine ait EEG Sinyallerinin 2-B Öznitelikler ve Yarı Olasılıksal Dağılım Modelleri ile Sınıflandırılması. Doktora Tezi, Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Trabzon.
  • Yilmaz, C. M., Yilmaz, B. H., Kose, C. MI-BMPI motor imagery brain–mobile phone dataset and performance evaluation of voting ensembles utilizing QPDM. Neural Comput & Applic 37, 4679–4696 (2025). https://doi.org/10.1007/s00521-024-10917-5
  • Yuan, H., Li, Y., Yang, J. et al. (2021). State of the art of non-invasive electrode materials for brain–computer interface. Micromachines, 12(12), 1521. https://doi.org/10.3390/mi12121521
  • Zhang, X., Wang, Y., Tang, Y. et al. Adaptive filter of frequency bands based coordinate attention network for EEG-based motor imagery classification. Health Inf Sci Syst 12, 11 (2024). https://doi.org/10.1007/s13755-024-00270-1
  • Zhao, G., Zhang, Y., Ge, Y. (2018). Frontal EEG asymmetry and middle line power difference in discrete emotions. Frontiers in behavioral neuroscience, 12, 225, https://www.doi.org/10.3389/fnbeh.2018.00225
  • Zhao K, Zhang Z, Wen H, Scano A. Intra-Subject and Inter-Subject Movement Variability Quantified with Muscle Synergies in Upper-Limb Reaching Movements. Biomimetics (Basel). 2021 Oct 20;6(4):63. doi: 10.3390/biomimetics6040063. PMID: 34698082; PMCID: PMC8544238.
  • Zheng WL, Liu W, Lu Y, Lu BL, Cichocki A. EmotionMeter: A Multimodal Framework for Recognizing Human Emotions. IEEE Trans Cybern. 2019 49 (3), 1110-1122, https://www.doi.org/10.1109/TCYB.2018.2797176
  • Zheng, W. L., Lu, B. L. (2015). Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Transactions on autonomous mental development, 7(3), 162-175., https://www.doi.org/10.1109/TAMD.2015.2431497
There are 71 citations in total.

Details

Primary Language Turkish
Subjects Computer Software, Biomedical Sciences and Technology, Biomedical Imaging, Engineering Practice
Journal Section Articles
Authors

Çağatay Murat Yılmaz 0000-0002-6513-7337

Bahar Hatipoğlu Yılmaz 0000-0002-7055-8945

Publication Date June 15, 2025
Submission Date January 23, 2025
Acceptance Date May 21, 2025
Published in Issue Year 2025 Volume: 15 Issue: 2

Cite

APA Yılmaz, Ç. M., & Hatipoğlu Yılmaz, B. (2025). Mobil EEG Cihazları ile Veri Toplama: Metodolojik Bir Kılavuz ve Uygulamalarla Değerlendirme. Karadeniz Fen Bilimleri Dergisi, 15(2), 897-922. https://doi.org/10.31466/kfbd.1625810
 Download Cover Image

by-nc-sa.pngThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.