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International Journal for Research in Applied Science & Engineering Technology (IJRASET)

ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538

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Volume 11 Issue II Feb 2023- Available at www.ijraset.com

Further research is needed to advance the field and overcome the challenges associated with BCI implementation in this population. Ethical and privacy concerns must also be addressed to ensure the responsible use of BCI technology for individuals with Alzheimer's disease. The review highlights the current state of BCI research for individuals with Alzheimer's disease and the need for continued investigation to realize the full potential of BCI technology for this population.

Refereces

[1] Remigiusz J. Rak, Marcin Kołodziej, Andrzej Majkowski“brain-computer interface as measurement and control system”Metrol. Meas. Syst., Vol. XIX (2012), No. 3, pp. 427-444.

[2] Mahshad Ouchani “A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer’s Disease Using EEG Signals” Hindawi BioMed Research International Volume 2021, Article ID 5425569, 15 pages https://doi.org/10.1155/2021/5425569

[3] R. Ducksbury, T. Whit_eld, and Z. Walker, ``SPECT/PET findings in Lewy body dementia,'' in PET and SPECT in Neurology. Berlin, Germany: Springer, 2014, pp. 373_415.

[4] D. A. Wolk, Z. Zhang, S. Boudhar, C. M. Clark, M. J. Pontecorvo, and S. E. Arnold, ``Amyloid imaging in Alzheimer's disease: Comparison of _orbetapir and Pittsburgh compound-B positron emission tomography,'' J. Neurol., Neurosurg. Psychiatry, vol. 83, no. 9, pp. 923_926, Sep. 2012.

[5] C. Easttom et al., "A Functional Model for Unifying Brain Computer Interface Terminology," in IEEE Open Journal of Engineering in Medicine and Biology, vol. 2, pp. 91-96, 2021, doi: 10.1109/OJEMB.2021.3057471.

[6] Dong-Kyun Han et al., “Domain Generalization for Session-Independent Brain-Computer Interface” in 2021 9th International Winter Conference on BrainComputer Interface (BCI).

[7] J. Fumanal-Idocin, Y. -K. Wang, C. -T. Lin, J. Fernández, J. A. Sanz and H. Bustince, "Motor-Imagery-Based Brain–Computer Interface Using Signal Derivation and Aggregation Functions," in IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 7944-7955, Aug. 2022, doi: 10.1109/TCYB.2021.3073210.

[8] B. Abibullaev and A. Zollanvari, "A Systematic Deep Learning Model Selection for P300-Based Brain–Computer Interfaces," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 5, pp. 2744-2756, May 2022, doi: 10.1109/TSMC.2021.3051136.

[9] Lance, Brent & Kerick, Scott & Ries, Anthony & Oie, Kelvin & McDowell, Kaleb. (2012). Brain Computer Interface Technologies in the Coming Decades. Proceedings of the IEEE. 100. 10.1109/JPROC.2012.2184830.

[10] Sheng-Ta Hsieh et al “A Brain Computer Interface for Attention Study” in 2019 in internaltional conference on robotics and control engineering.

[11] Abdelkader Nasreddine Belkacem et al.,“Brain Computer Interfaces for Improving the Quality ofLifeofOlderAdultsandElderlyPatients”Front.Neurosci.,30June2020Sec.NeuralTechnologyVolume 14

2020.

[12] Remigiusz J. Rak et al “Brain-Computer Interface As Measurement And Control System The Review Paper” Metrol. Meas. Syst., Vol. XIX (2012), No. 3, pp. 427-444.

[13] D. Manzak, G. Çetinel and A. Manzak, "Automated Classification of Alzheimer’s Disease using Deep Neural Network (DNN) by Random Forest Feature Elimination," 2019 14th International Conference on Computer Science & Education (ICCSE), Toronto, ON, Canada, 2019, pp. 1050-1053, doi: 10.1109/ICCSE.2019.8845325.

[14] Sofia de la Fuente Garcia et al., “Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review” Journal of Alzheimer’s Disease 78 (2020) 1547–1574 DOI 10.3233/JAD-200888 IOS Press

[15] Maria Luisa et al “Alzheimer’s disease and automatic speech analysis: a review, Expert Systems With Applications” (2020), Published by Elsevier Ltd.

[16] Melanie Fried-Oken, et al “Brain-Computer Interface to Enhance Attention in Alzheimer’s disease” Supplement award to R01DC009834 National Institute on Deafness and Other Communication Disorders Oregon Health & Science University 3/1/2018-2/28/20217.

[17] JAMAL F. HWAID et al., “Classification of Motor Imagery EEG Signals Based on Deep Autoencoder and Convolutional Neural Network Approach” ieee access May 9, 2022. Digital Object Identifier 10.1109/ACCESS.2022.3171906.

[18] Lee T-S, Goh SJA, Quek SY, Phillips R, Guan C, et al. (2013) “A Brain-Computer Interface Based Cognitive Training System for Healthy Elderly: A Randomized Control Pilot Study for Usability and Preliminary Efficacy.”PLoS ONE 8(11): e79419. doi:10.1371/journal.pone.0079419.

[19] Miltiadous, A.; Tzimourta, K.D.; Giannakeas, N.; Tsipouras, M.G.; Afrantou, T.; Ioannidis, P.; Tzallas, A.T. “Alzheimer’s Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods. Diagnostics” 2021, 11, 1437. https://doi.org/10.3390/diagnostics 11081437.

[20] Sitara Afzal et al.,“A Data Augmentation based Framework to Handle Class Imbalance Problem for Alzheimer’s Stage Detection” DOI 10.1109/ACCESS.2019.2932786, IEEE Access.

[21] Niago Moreira Nobre Leit et al “Deep Convolutional Autoencoder for EEG Noise Filtering” 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[22] Yang Qui et al., “Denoising Sparse Autoencoder Based Ictal EEG Classification” DOI 10.1109/TNSRE.2018.2864306, IEEE.

[23] HONGLI ZHANG et al., “Expression-EEG Based Collaborative Multimodal Emotion Recognition Using Deep AutoEncoder” September 21, 2020. Digital Object Identifier 10.1109/ACCESS.2020.3021994.

[24] C. Jia, X. Gao, B. Hong and S. Gao*, "Frequency and Phase Mixed Coding in SSVEP-Based Brain Computer Interface," in IEEE Transactions on Biomedical Engineering, vol. 58, no. 1, pp. 200-206, Jan. 2011, doi: 10.1109/TBME.2010.2068571.

[25] Rajdeep Ghosh et al “Automated eye blink artefact removal from EEG using support vector machine and autoencoder” ISSN 1751-9675 Received on 1st May 2018 Revised 7th July 2018 Accepted on 13th August 2018 E-First on 2nd November 2018 doi: 10.1049/iet-spr.2018.5111 www.ietdl.org.

[26] Hoang Nguyen et al “An Introduction to Deep Learning Research for Alzheimer’s Disease” June 15,2021 at 12:09:09 UTC from IEEE Xplore.

[27] Liberati, Giulia & Dalboni, Josue & van der Heiden, Linda & Raffone, Antonino & Birbaumer, Niels & Belardinelli, Marta & Sitaram, Ranganatha. (2012). “Toward a Brain-Computer Interface for Alzheimer's Disease Patients by Combining Classical Conditioning and Brain State Classification. Journal of Alzheimer's disease” JAD. 31. S211-20. 10.3233/JAD-2012-112129.

[28] Ehrlich, S.; Guan, C.; Cheng, G. A closed-loop brain-computer music interface for continuous affective interaction. In Proceedings of the 2017 International Conference on Orange Technologies (ICOT), Singapore, 8–10 September 2017; pp. 176–179.

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