Brain Computer Interface (BCI) Machine Learning Process: A Review

Main Article Content

Shofi Afif Hanafi
Hisyam Bin Abdul Rahman
Dwika Ananda Agustina Pertiwi
Much Aziz Muslim

Abstract

The abstraction of Brain Computer Interface (BCI) is a communication and control system that translated human mind thoughts into real-world interaction without any use of neural pathways and muscles. BCI is used as a tool to help person that suffered from impairment to be able do their daily activities independently. In general, BCI process its signal through several process such as pre-processing, and classification. However, providing information of pre-processing and classification process is barely found. Therefore, in this review paper we present the various pre-processing and classification methods that used in the BCI system application.


 

Article Details

How to Cite
Hanafi, S. A., Abdul Rahman, H. B., Pertiwi, D. A. A., & Muslim, M. A. (2023). Brain Computer Interface (BCI) Machine Learning Process: A Review. Journal of Electronics Technology Exploration, 1(1), 29-35. https://doi.org/10.52465/joetex.v1i1.189
Section
Articles

References

T. Trakoolwilaiwan, B. Behboodi, J. Lee, K. Kim, and J.-W. Choi, “Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain–computer interface: Three-class classification of rest, right-, and left-hand motor execution,” Neurophotonics, vol. 5, no. 01, p. 1, 2017. doi:10.1117/1.nph.5.1.011008

R. A. Khan et al., “FNIRS-based Neurorobotic interface for Gait Rehabilitation,” Journal of NeuroEngineering and Rehabilitation, vol. 15, no. 1, 2018. doi:10.1186/s12984-018-0346-2

K. Gui, H. Liu, and D. Zhang, “Toward multimodal human–robot interaction to enhance active participation of users in Gait Rehabilitation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 11, pp. 2054–2066, 2017. doi:10.1109/tnsre.2017.2703586

F. Lotte et al., “A review of classification algorithms for EEG-based brain–computer interfaces: A 10 Year update,” Journal of Neural Engineering, vol. 15, no. 3, p. 031005, 2018. doi:10.1088/1741-2552/aab2f2

S. Fazli et al., “Enhanced performance by a hybrid nirs–EEG brain computer interface,” NeuroImage, vol. 59, no. 1, pp. 519–529, 2012. doi:10.1016/j.neuroimage.2011.07.084

A. L. Dallora, S. Eivazzadeh, E. Mendes, J. Berglund, and P. Anderberg, “Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review,” PLOS ONE, vol. 12, no. 6, 2017. doi:10.1371/journal.pone.0179804

N. K. Qureshi et al., “Enhancing classification performance of functional near-infrared spectroscopy- brain–computer interface using adaptive estimation of general linear model coefficients,” Frontiers in Neurorobotics, vol. 11, 2017. doi:10.3389/fnbot.2017.00033

Y. Gao, B. Jia, M. Houston, and Y. Zhang, “Hybrid EEG-fnirs brain computer interface based on common spatial pattern by using EEG-informed general Linear Model,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–10, 2023. doi:10.1109/tim.2023.3276509

M. A. Tanveer, M. J. Khan, M. J. Qureshi, N. Naseer, and K.-S. Hong, “Enhanced drowsiness detection using Deep learning: An fNIRS study,” IEEE Access, vol. 7, pp. 137920–137929, 2019. doi:10.1109/access.2019.2942838

Abraham. Savitzky and M. J. Golay, “Smoothing and differentiation of data by simplified least squares procedures.,” Analytical Chemistry, vol. 36, no. 8, pp. 1627–1639, 1964. doi:10.1021/ac60214a047

C. Li, M. Su, J. Xu, H. Jin, and L. Sun, “A between-subject fnirs-BCI study on detecting self-regulated intention during walking,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 2, pp. 531–540, 2020. doi:10.1109/tnsre.2020.2965628

J. Hennrich, C. Herff, D. Heger, and T. Schultz, “Investigating deep learning for fNIRS based BCI,” 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015. doi:10.1109/embc.2015.7318984

U. Asgher et al., “Assessment and classification of mental workload in the prefrontal cortex (PFC) using fixed-value modified beer-Lambert Law,” IEEE Access, vol. 7, pp. 143250–143262, 2019. doi:10.1109/access.2019.2944965

V. Mihajlovic, S. Patki, and B. Grundlehner, “The impact of head movements on EEG and contact impedance: An adaptive filtering solution for motion artifact reduction,” 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014. doi:10.1109/embc.2014.6944763

H. Hamid et al., “Analyzing classification performance of fNIRS-BCI for gait rehabilitation using Deep Neural Networks,” Sensors, vol. 22, no. 5, p. 1932, 2022. doi:10.3390/s22051932

Janani. A, Sasikala. M, H. Chhabra, N. Shajil, and G. Venkatasubramanian, “Investigation of deep convolutional neural network for classification of motor imagery fNIRS signals for BCI applications,” Biomedical Signal Processing and Control, vol. 62, p. 102133, 2020. doi:10.1016/j.bspc.2020.102133

H. Zhang, B. Chen, Z. Wang, and H. Liu, “Deep Max-margin discriminant projection,” IEEE Transactions on Cybernetics, vol. 49, no. 7, pp. 2454–2466, 2019. doi:10.1109/tcyb.2018.2831792

Q. Bi, K. E. Goodman, J. Kaminsky, and J. Lessler, “What is machine learning? A Primer for the epidemiologist,” American Journal of Epidemiology, 2019. doi:10.1093/aje/kwz189

J. Shin et al., “Open access dataset for EEG+NIRS single-trial classification,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 10, pp. 1735–1745, 2017. doi:10.1109/tnsre.2016.2628057

P. W. Dans, S. D. Foglia, and A. J. Nelson, “Data Processing in functional near-infrared spectroscopy (FNIRS) Motor Control Research,” Brain Sciences, vol. 11, no. 5, p. 606, 2021. doi:10.3390/brainsci11050606

B. R. Groff, P. Antonellis, K. K. Schmid, B. A. Knarr, and N. Stergiou, “Stride-time variability is related to sensorimotor cortical activation during forward and backward walking,” Neuroscience Letters, vol. 692, pp. 150–158, 2019. doi:10.1016/j.neulet.2018.10.022

N. Y. Sattar et al., “FNIRS-based upper limb motion intention recognition using an artificial neural network for transhumeral amputees,” Sensors, vol. 22, no. 3, p. 726, 2022. doi:10.3390/s22030726

P. Ortega, T. Zhao, and A. A. Faisal, “HYGRIP: Full-stack characterization of neurobehavioral signals (fNIRS, EEG, EMG, force, and breathing) during a bimanual grip force control task,” Frontiers in Neuroscience, vol. 14, 2020. doi:10.3389/fnins.2020.00919

W.-L. Chen et al., “Functional near-infrared spectroscopy and its clinical application in the field of neuroscience: Advances and future directions,” Frontiers in Neuroscience, vol. 14, 2020. doi:10.3389/fnins.2020.00724

J. Liu, T. Song, Z. Shu, J. Han, and N. Yu, “FNIRS feature extraction and classification in grip-force tasks,” 2021 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2021. doi:10.1109/robio54168.2021.9739514

N. Sengupta, C. B. McNabb, N. Kasabov, and B. R. Russell, “Integrating space, time, and orientation in spiking neural networks: A case study on multimodal brain data modeling,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 11, pp. 5249–5263, 2018. doi:10.1109/tnnls.2018.2796023

L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain Computer Interfaces, a review,” Sensors, vol. 12, no. 2, pp. 1211–1279, 2012. doi:10.3390/s120201211

N. Naseer and K.-S. Hong, “FNIRS-based brain-computer interfaces: A Review,” Frontiers in Human Neuroscience, vol. 9, 2015. doi:10.3389/fnhum.2015.00003

T. Ma et al., “FNIRS signal classification based on deep learning in rock-paper-scissors imagery task,” Applied Sciences, vol. 11, no. 11, p. 4922, 2021. doi:10.3390/app11114922

N. A. Alzahab et al., “Hybrid deep learning (hdl)-based brain-computer interface (BCI) systems: A systematic review,” Brain Sciences, vol. 11, no. 1, p. 75, 2021. doi:10.3390/brainsci11010075

S. Rasheed, “A review of the role of machine learning techniques towards brain–computer interface applications,” Machine Learning and Knowledge Extraction, vol. 3, no. 4, pp. 835–862, 2021. doi:10.3390/make3040042

Z. Koudelková and M. Strmiska, “Introduction to the identification of brain waves based on their frequency,” MATEC Web of Conferences, vol. 210, p. 05012, 2018. doi:10.1051/matecconf/201821005012

J. J. Shih, D. J. Krusienski, and J. R. Wolpaw, “Brain-computer interfaces in medicine,” Mayo Clinic Proceedings, vol. 87, no. 3, pp. 268–279, 2012. doi:10.1016/j.mayocp.2011.12.008

Abstract viewed = 274 times