A Review on Examination Methods of Types of Working Memory and Cerebral Cortex in EEG Signals

Subject Areas : Majlesi Journal of Telecommunication Devices

Mehran Emadi ¹ , Mohsen Karimi ² , Fatemeh Davoudi ³

1 - Department of Electrical Engineering, Mobarkeh Branch, Islamic Azad University, Mobarkeh, Isfahan, Iran
2 - Department of Bioelectric and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan, Iran
3 - Iran university science and technology (IUST) School of Electrical Engineering, Tehran, Iran

Received: 2023-07-10 Accepted : 2023-08-23 Published : 2023-09-01

Keywords: working memory, Brain, Cerebral cortex, Electroencephalography,

Abstract :

Brain is the most important organ in human body. All of our memories are saved on brain. Brain activity is analyzed by electroencephalogram signals (EEG). Brain activity and memory signal represent brain activity that can be recorded in different brain regions. Electroencephalography signal analysis can provide complete and comprehensive information about brain activity. Working of brain memory as an activity is analyzed by EEG. The main purpose of this research is to review the types of memory and especially working memory in humans by processing of EEG. In this regard, memory and types of memory have been discussed at the beginning. Then the brain activity and memory signal, recording method, electrode placement, brain potentials are discussed. Then, the complexity of the brain activity and memory signal in memory has been investigated. Then, memory and its relationship with brain activity and memory signal have been discussed. Areas affected by memory are expressed in the brain. After the researches about brain activity and memory signals in memory, it has been investigated.

References:

• [1] Mahmudi, M. Soleimani, and M. Naderi, "Some Properties of the Maximal Graph of a Commutative Ring," Southeast Asian Bulletin of Mathematics, vol. 43, no. 4, 2019.


• [2] Heidari, and M. Ramezanpour, “Reduction of intra-coding time for HEVC based on temporary direction map,” Journal of Real-Time Image Processing, vol. 17, pp. 567-579, 2020..


• [3] R. Khan et al., "Authentication through gender classification from iris images using support vector machine," Microscopy research and technique, vol. 84, no. 11, pp. 2666-2676, 2021.


• [4] Aryanmehr, M. Karimi, and F. Z. Boroujeni, "CVBL IRIS gender classification database image processing and biometric research, computer vision and biometric laboratory (CVBL)," in 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), 2018, pp. 433-438: IEEE.


• [5] Raftarai, R. R. Mahounaki, M. Harouni, M. Karimi, and S. K. Olghoran, "Predictivemodels of hospital readmission rate using the improved AdaBoost in COVID-19," in Intelligent Computing Applications for COVID-19: CRC Press, 2021, pp. 67-86.


• [6] Emadi, Z. Jafarian Dehkordi, and M. Iranpour Mobarakeh, "Improving the Accuracy of Brain Tumor Identification in Magnetic Resonanceaging using Super-pixel and Fast Primal Dual Algorithm," International Journal of Engineering, vol. 36, no. 3, pp. 505-512, 2023.


• [7] Nardone et al., "Visuomotor integration in early Alzheimer's disease: A TMS study," Journal of the neurological sciences, p. 120129, 2022.


• [8] J. Moshayedi et al., "E-Nose design and structures from statistical analysis to application in robotic: a compressive review," EAI Endorsed Transactions on AI and Robotics, vol. 2, no. 1, pp. e1-e1, 2023.


• [9] Rehman, M. Harouni, M. Karimi, T. Saba, S. A. Bahaj, and M. J. Awan, "Microscopic retinal blood vessels detection and segmentation using support vector machine and K‐nearest neighbors," Microscopy research and technique, vol. 85, no. 5, pp. 1899-1914, 2022.


• Karimi, M. Harouni, A. Nasr, and N. Tavakoli, "Automatic lung infection segmentation of covid-19 in CT scan images," in Intelligent Computing Applications for COVID-19: CRC Press, 2021, pp. 235-253.


• A. Mousavizadeh Mobarakeh and M. Emadi, "Improvement of the Identification Rate using Finger Veins based on the Enhanced Maximum Curvature Method using Morphological Operators," Majlesi Journal of Telecommunication Devices, vol. 11, no. 1, 2022.


• Emadi and M. Emadi, "Human face detection in color images using fusion of Ada Boost and LBP feature," Majlesi Journal of Telecommunication Devices, vol. 9, no. 1, pp. 23-33, 2020.


• Heo and G. Yoon, "EEG studies on physical discomforts induced by virtual reality gaming," Journal of Electrical Engineering & Technology, vol. 15, no. 3, pp. 1323-1329, 2020.


• Soleimani, F. Mahmudi, and M. H. Naderi, "On the Maximal Graph of a Commutative Ring," Mathematics Interdisciplinary Research, 2021.


• Soleimani, M. H. Naderi, and A. R. Ashrafi, "TENSOR PRODUCT OF THE POWER GRAPHS OF SOME FINITE RINGS," Facta Universitatis, Series: Mathematics and Informatics, pp. 101-122, 2019.


• Karimi, M. Harouni, and S. Rafieipour, "Automated medical image analysis in digital mammography," in Artificial intelligence and internet of things: CRC Press, 2021, pp. 85-116.


• Koshino, T. Minamoto, K. Yaoi, M. Osaka, and N. Osaka, "Coactivation of the default mode network regions and working memory network regions during task preparation," Scientific reports, vol. 4, no. 1, pp. 1-8, 2014.


• Karimi, M. Harouni, E. I. Jazi, A. Nasr, and N. Azizi, "Improving monitoring and controlling parameters for alzheimer’s patients based on iomt," in Prognostic models in healthcare: Ai and statistical approaches: Springer, 2022, pp. 213-237.


• Harouni, M. Karimi, and S. Rafieipour, "Precise segmentation techniques in various medical images," Artificial Intelligence and Internet of Things: Applications in Smart Healthcare, vol. 117, 2021.


• Borhani et al., "Gauging Working Memory Capacity From Differential Resting Brain Oscillations in Older Individuals With A Wearable Device," Frontiers in Aging Neuroscience, vol. 13, p. 36, 2021.


• H. Cattan, A. Andreev, C. Mendoza, and M. Congedo, "A comparison of mobile VR display running on an ordinary smartphone with standard PC display for P300-BCI stimulus presentation," IEEE Transactions on Games, 2019.


• Badura, A. Masłowska, A. Myśliwiec, and E. Piętka, "Multimodal Signal Analysis for Pain Recognition in Physiotherapy Using Wavelet Scattering Transform," Sensors, vol. 21, no. 4, p. 1311, 2021.


• Wan, Q. Wang, K. Su, C. Dong, W. Song, and M. Pang, "Measuring the Impacts of Virtual Reality Games on Cognitive Ability Using EEG Signals and Game Performance Data," IEEE Access, vol. 9, pp. 18326-18344, 2021.


• Daniel and S. Pollmann, "Striatal activations signal prediction errors on confidence in the absence of external feedback," Neuroimage, vol. 59, no. 4, pp. 3457-3467, 2012.


• Dabas, C. Sethi, C. Dua, M. Dalawat, and D. Sethia, "Emotion classification using EEG signals," in Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence, 2018, pp. 380-384.


• Cattan, A. Andreev, C. Mendoza, and M. Congedo, "The impact of passive head-mounted virtual reality devices on the quality of EEG signals," in Workshop on Virtual Reality Interaction and Physical Simulation, 2018: The Eurographics Association.


• Harouni, M. Karimi, A. Nasr, H. Mahmoudi, and Z. Arab Najafabadi, "Health monitoring methods in heart diseases based on data mining approach: A directional review," in Prognostic models in healthcare: Ai and statistical approaches: Springer, 2022, pp. 115-159.


• Mahmudi and M. Soleimani, "Some results on Maximal Graph of a Commutative Ring," 2016.


• [29] Jafarzadeh and M. Soleimani, "NON-VANISHING ELEMENTS AND ZEROS OF CHARACTERS OF FINITE GROUPS: A REVIEW," in 42nd Annual Iranian Mathematics Conference, 2011.


• A. Dudchenko, "An overview of the tasks used to test working memory in rodents," Neuroscience & Biobehavioral Reviews, vol. 28, no. 7, pp. 699-709, 2004.


• Klimesch, "EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis," Brain research reviews, vol. 29, no. 2-3, pp. 169-195, 1999.


• Li, Z. Deng, and J. Zhang, "Function of EEG temporal complexity analysis in neural activities measurement," in International Symposium on Neural Networks, 2009, pp. 209-218: Springer.


• Najafabadi, and M. Ramezanpour, “Mass center direction-based decision method for intraprediction in HEVC standard,” Journal of Real-Time Image Processing, vol. 17, no. 5, pp. 1153-1168, 2020..


• Kolata and S. Kolata, "A model of working memory capacity in the radial-arm maze task," Journal of Mathematical Psychology, vol. 53, no. 4, pp. 242-252, 2009.


• Talebi, A. M. Nasrabadi, and T. Curran, "Investigation of changes in EEG complexity during memory retrieval: the effect of midazolam," Cognitive neurodynamics, vol. 6, no. 6, pp. 537-546, 2012.


• Wang, M. Tugcu, J. E. Hunter, and D. M. Wilkes, "Exploration of configural representation in landmark learning using working memory toolkit," Pattern recognition letters, vol. 30, no. 1, pp. 66-79, 2009.


• Nikolin, Y. Y. Tan, A. Schwaab, A. Moffa, C. K. Loo, and D. Martin, "An investigation of working memory deficits in depression using the n-back task: A systematic review and meta-analysis," Journal of Affective Disorders, 2021.


• Sarnthein, H. Petsche, P. Rappelsberger, G. Shaw, and A. Von Stein, "Synchronization between prefrontal and posterior association cortex during human working memory," Proceedings of the National Academy of Sciences, vol. 95, no. 12, pp. 7092-7096, 1998.


• Lui, K. F. Lui, A. C.-N. Wong, and J. P. Rosenfeld, "Suppression of 12-Hz SSVEPs when viewing familiar faces: An electrophysiological index to detect recognition," International Journal of Psychophysiology, vol. 133, pp. 159-168, 2018.


• Maurer, S. Brem, M. Liechti, S. Maurizio, L. Michels, and D. Brandeis, "Frontal midline theta reflects individual task performance in a working memory task," Brain topography, vol. 28, no. 1, pp. 127-134, 2015.


• Tremmel, "Estimating Cognitive Workload in an Interactive Virtual Reality Environment Using Electrophysiological and Kinematic Activity," Old Dominion University, 2019.


• W. Howard et al., "Gamma oscillations correlate with working memory load in humans," Cerebral cortex, vol. 13, no. 12, pp. 1369-1374, 2003.


• B. Sala and S. M. Courtney, "Binding of what and where during working memory maintenance," Cortex, vol. 43, no. 1, pp. 5-21, 2007.


• J. Haarmann and K. A. Cameron, "Active maintenance of sentence meaning in working memory: Evidence from EEG coherences," International journal of psychophysiology, vol. 57, no. 2, pp. 115-128, 2005.


• Honkanen, S. Rouhinen, S. H. Wang, J. M. Palva, and S. Palva, "Gamma oscillations underlie the maintenance of feature-specific information and the contents of visual working memory," Cerebral cortex, vol. 25, no. 10, pp. 3788-3801, 2015.


• Concatto, E. J. Legal, and A. R. G. Ramirez, "On-line Measurement of Working Memory in Individuals with Aphasia using Electroencephalographic Signals," in 2021 16th Iberian Conference on Information Systems and Technologies (CISTI), 2021, pp. 1-4: IEEE.


• Berger, S. Omer, T. Minarik, A. Sterr, and P. Sauseng, "Interacting memory systems—does EEG alpha activity respond to semantic long-term memory access in a working memory task?," Biology, vol. 4, no. 1, pp. 1-16, 2015.


• A. Meltzer et al., "Effects of working memory load on oscillatory power in human intracranial EEG," Cerebral Cortex, vol. 18, no. 8, pp. 1843-1855, 2008.


• Abásolo, R. Hornero, P. Espino, J. Poza, C. I. Sánchez, and R. de la Rosa, "Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy," Clinical neurophysiology, vol. 116, no. 8, pp. 1826-1834, 2005.


• Zarjam, J. Epps, F. Chen, and N. H. Lovell, "Classification of working memory load using wavelet complexity features of EEG signals," in International Conference on Neural Information Processing, 2012, pp. 692-699: Springer.