تقنيات الذكاء الاصطناعي لفك تشفير وتمثيلات اللغه من نشاط الدماغ القائم على تخطيط كهربية الدماغ:مراجعه شامله

Y. Lin and P. J. Hsieh, “Neural decoding of speech with semantic-based classification,” Cortex, vol. 154, pp. 231–240, Sep. 2022, doi: 10.1016/j.cortex.2022.05.018.

A. Soman, C. R. Madhavan, K. Sarkar, and S. Ganapathy, “An EEG study on the brain representations in language learning,” Biomed Phys Eng Express, vol. 5, no. 2, Feb. 2019, doi: 10.1088/2057-1976/ab0243.

K. Morgan-Short, “Electrophysiological approaches to understanding second language acquisition: A field reaching its potential,” 2014, Cambridge University Press. doi: 10.1017/S026719051400004X.

J. Önerud, “EEG-Based Speech Decoding Using a Machine Learning Pipeline,” 2023.

M. Shi, C. Wang, X. Z. Li, M. Q. Li, L. Wang, and N. G. Xie, “Eeg signal classification based on svm with improved squirrel search algorithm,” Biomedizinische Technik, vol. 66, no. 2, pp. 137–152, Apr. 2021, doi: 10.1515/bmt-2020-0038.

Y. Zhao, Y. Chen, K. Cheng, and W. Huang, “Artificial intelligence based multimodal language decoding from brain activity: A review,” Sep. 01, 2023, Elsevier Inc. doi: 10.1016/j.brainresbull.2023.110713.

X. Zhang et al., “The combination of brain-computer interfaces and artificial intelligence: applications and challenges,” Ann Transl Med, vol. 8, no. 11, pp. 712–712, Jun. 2020, doi: 10.21037/atm.2019.11.109.

J. Kufel et al., “What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine,” Aug. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/diagnostics13152582.

V. A. Maksimenko et al., “Artificial neural network classification of motor-related EEG: An increase in classification accuracy by reducing signal complexity,” Complexity, vol. 2018, 2018, doi: 10.1155/2018/9385947.

M. Ferrante et al., “Multimodal decoding of human brain activity into images and text.”

M. K. Islam, P. Ghorbanzadeh, and A. Rastegarnia, “Probability mapping based artifact detection and removal from single-channel EEG signals for brain–computer interface applications,” J Neurosci Methods, vol. 360, Aug. 2021, doi: 10.1016/j.jneumeth.2021.109249.

Z. Wang and H. Ji, “Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification,” Dec. 2021, [Online]. Available: http://arxiv.org/abs/2112.02690

H. Liu, D. Hajialigol, B. Antony, A. Han, and X. Wang, “EEG2TEXT: Open Vocabulary EEG-to-Text Decoding with EEG Pre-Training and Multi-View Transformer,” May 2024, [Online]. Available: http://arxiv.org/abs/2405.02165

S. Coelli et al., “Selecting methods for a modular EEG pre-processing pipeline: An objective comparison,” Biomed Signal Process Control, vol. 90, Apr. 2024, doi: 10.1016/j.bspc.2023.105830.

S. Maria and C. J, “Preprocessing Pipelines for EEG,” SHS Web of Conferences, vol. 139, p. 03029, 2022, doi: 10.1051/shsconf/202213903029.

M. Orban, M. Elsamanty, K. Guo, S. Zhang, and H. Yang, “A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application,” Dec. 01, 2022, MDPI. doi: 10.3390/bioengineering9120768.

J. Yang, X. Huang, H. Wu, and X. Yang, “EEG-based emotion classification based on Bidirectional Long Short-Term Memory Network,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 491–504. doi: 10.1016/j.procs.2020.06.117.

S. Kamil Gatfan, “A Review on Deep Learning For Electroencephalogram Signal Classification,” Journal of Al-Qadisiyah for Computer Science and Mathematics, vol. 16, no. 1, pp. 137–151, Mar. 2024, doi: 10.29304/jqcsm.2024.16.11453.

B. Yildirim, O. Ulkir, M. Kaya, A. K. Singh, and S. Krishnan, “Trends in EEG signal feature extraction applications.”

H. Amrani, D. Micucci, and P. Napoletano, “Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decoding,” Nov. 2023, doi: 10.1109/JBHI.2024.3416066.

S. A. Murad and N. Rahimi, “Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Text,” Apr. 2024, doi: 10.1109/TCDS.2024.3462452.

N. Hollenstein et al., “Decoding EEG Brain Activity for Multi-Modal Natural Language Processing,” Front Hum Neurosci, vol. 15, Jul. 2021, doi: 10.3389/fnhum.2021.659410.

D. Alonso-Vázquez, O. Mendoza-Montoya, R. Caraza, H. R. Martinez, and J. M. Antelis, “EEG-Based Classification of Spoken Words Using Machine Learning Approaches,” Computation, vol. 11, no. 11, Nov. 2023, doi: 10.3390/computation11110225.

Y. Qiu, H. Liu, and M. Zhao, “A Review of Brain–Computer Interface-Based Language Decoding: From Signal Interpretation to Intelligent Communication,” Jan. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/app15010392.

A. L. Giraud and Y. Su, “Reconstructing language from brain signals and deconstructing adversarial thought-reading,” Jul. 18, 2023, Cell Press. doi: 10.1016/j.xcrm.2023.101115.

S. J. Forkel and P. Hagoort, “Redefining language networks: connectivity beyond localised regions,” Dec. 01, 2024, Springer Science and Business Media Deutschland GmbH. doi: 10.1007/s00429-024-02859-4.

A. D. Friederici, “THE BRAIN BASIS OF LANGUAGE PROCESSING: FROM STRUCTURE TO FUNCTION,” Physiol Rev, vol. 91, pp. 1357–1392, 2011, doi: 10.1152/physrev.00006.2011.-Lan.

M. Brown, M. K. Tanenhaus, and L. Dilley, “Syllable Inference as a Mechanism for Spoken Language Understanding,” Top Cogn Sci, vol. 13, no. 2, pp. 351–398, Apr. 2021, doi: 10.1111/tops.12529.

F. Bai, A. S. Meyer, and A. E. Martin, “Neural dynamics differentially encode phrases and sentences during spoken language comprehension,” PLoS Biol, vol. 20, no. 7, Jul. 2022, doi: 10.1371/journal.pbio.3001713.

Pawan and R. Dhiman, “Machine learning techniques for electroencephalogram based brain-computer interface: A systematic literature review,” Measurement: Sensors, vol. 28, Aug. 2023, doi: 10.1016/j.measen.2023.100823.

W. K. Mutlag, S. K. Ali, Z. M. Aydam, and B. H. Taher, “Feature Extraction Methods: A Review,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Aug. 2020. doi: 10.1088/1742-6596/1591/1/012028.

M. J. Antony et al., “Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis,” Sensors, vol. 22, no. 19, Oct. 2022, doi: 10.3390/s22197596.

G. Hamid Zghair and D. Shaheed Al-Azzawi, “Comparing emotion classification: machine learning algorithms and hybrid model with support vector machines,” IAES International Journal of Artificial Intelligence, vol. 13, no. 3, pp. 3671–3685, Sep. 2024, doi: 10.11591/ijai.v13.i3.pp3671-3685.

D. S. Al-Azzawi, “Application and evaluation of the neural network in gearbox,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 18, no. 1, pp. 19–29, Feb. 2020, doi: 10.12928/TELKOMNIKA.v18i1.13760.

D. S. Al-Azzawi, “Evaluation of genetic algorithm optimization in machine learning,” Journal of Information Science and Engineering, vol. 36, no. 2, pp. 231–241, 2020, doi: 10.6688/JISE.202003_36(2).0004.

I. akram fadhil Alzuabidi, “Application of Machine Learning Techniques for Countering Side-Channel Attacks in Cryptographic Systems,” AlKadhim Journal for Computer Science, vol. 2, no. 3, pp. 1–9, Sep. 2024, doi: 10.61710/kjcs.v2i3.78.

H. Adnan Mohammed and A. Sadeq Jaafar, “Hybrid PSO-Bagging Approach for Efficient and Accurate Network Anomaly Detection,” Alkadhim Journal for Computer Science, vol. 3, no. 1, 2025, doi: 10.53523/ijoirVolxIxIDxx.

J. Homepage and M. Y. Abdullah, “Wasit Journal of Computer and Mathematics Science Real time handwriting recognition system using CNN algorithms”, doi: 10.31185/wjcm.157.

Y.-E. Lee and S.-H. Lee, “EEG-Transformer: Self-attention from Transformer Architecture for Decoding EEG of Imagined Speech”, doi: 10.48550/arXiv.2112.09239.

B. Lim, S. Arık, N. Loeff, and T. Pfister, “Temporal Fusion Transformers for interpretable multi-horizon time series forecasting,” Int J Forecast, vol. 37, no. 4, pp. 1748–1764, Oct. 2021, doi: 10.1016/j.ijforecast.2021.03.012.

Z. Cheng, X. Bu, Q. Wang, T. Yang, and J. Tu, “EEG-based emotion recognition using multi-scale dynamic CNN and gated transformer,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-82705-z.

A. G. Lazcano-Herrera, R. Q. Fuentes-Aguilar, A. Ramirez-Morales, and M. Alfaro-Ponce, “BiLSTM and SqueezeNet with Transfer Learning for EEG Motor Imagery Classification: Validation with Own Dataset,” IEEE Access, vol. 11, pp. 136422–136436, 2023, doi: 10.1109/ACCESS.2023.3328254.

N. Affolter, B. Egressy, D. Pascual, and R. Wattenhofer, “Brain2Word: Decoding Brain Activity for Language Generation,” Sep. 2020, [Online]. Available: http://arxiv.org/abs/2009.04765

S. Jia, Y. Hou, Y. Shi, and Y. Li, “Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals,” Jun. 2020, [Online]. Available: http://arxiv.org/abs/2007.13484

Y. Roy, H. Banville, I. Albuquerque, A. Gramfort, T. H. Falk, and J. Faubert, “Deep learning-based electroencephalography analysis: A systematic review,” Aug. 14, 2019, Institute of Physics Publishing. doi: 10.1088/1741-2552/ab260c.

E. Vafaei and M. Hosseini, “Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification,” Mar. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/s25051293.

I. Rakhmatulin, M. S. Dao, A. Nassibi, and D. Mandic, “Exploring Convolutional Neural Network Architectures for EEG Feature Extraction,” Sensors, vol. 24, no. 3, Feb. 2024, doi: 10.3390/s24030877.

M. Sato, K. Tomeoka, I. Horiguchi, K. Arulkumaran, R. Kanai, and S. Sasai, “Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Data,” Jul. 2024, [Online]. Available: http://arxiv.org/abs/2407.07595

Z. Wan, M. Li, S. Liu, J. Huang, H. Tan, and W. Duan, “EEGformer: A transformer–based brain activity classification method using EEG signal,” Front Neurosci, vol. 17, 2023, doi: 10.3389/fnins.2023.1148855.

T. Y.-H. Chen, Y. Chen, P. Soederhaell, S. Agrawal, and K. Shapovalenko, “Decoding EEG Speech Perception with Transformers and VAE-based Data Augmentation,” Jan. 2025, [Online]. Available: http://arxiv.org/abs/2501.04359

H. Jo, Y. Yang, J. Han, Y. Duan, H. Xiong, and W. H. Lee, “Are EEG-to-Text Models Working?,” May 2024, [Online]. Available: http://arxiv.org/abs/2405.06459

A. Mishra, S. Shukla, J. Torres, J. Gwizdka, and S. Roychowdhury, “Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs),” Oct. 2024, [Online]. Available: http://arxiv.org/abs/2410.07507

K. Haritha, S. Shailesh, M. V. Judy, K. S. Ravichandran, R. Krishankumar, and A. H. Gandomi, “A novel neural network model with distributed evolutionary approach for big data classification,” Sci Rep, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-37540-z.

X. Mou et al., “ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding,” Sci Data, vol. 11, no. 1, Dec. 2024, doi: 10.1038/s41597-024-03398-7.

A. Défossez, C. Caucheteux, J. Rapin, O. Kabeli, and J. R. King, “Decoding speech perception from non-invasive brain recordings,” Nat Mach Intell, vol. 5, no. 10, pp. 1097–1107, Oct. 2023, doi: 10.1038/s42256-023-00714-5.

M. Rehman et al., “Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning,” Sensors, vol. 24, no. 21, Nov. 2024, doi: 10.3390/s24216965.

C. Caucheteux, A. Gramfort, and J. R. King, “Deep language algorithms predict semantic comprehension from brain activity,” Sci Rep, vol. 12, no. 1, Dec. 2022, doi: 10.1038/s41598-022-20460-9.

Z. Lamprou and Y. Moshfeghi, “On Creating A Brain-To-Text Decoder,” Jan. 2025, [Online]. Available: http://arxiv.org/abs/2501.06326

A. AZEEZ, “Automated Emotion Recognition Using Hybrid CNN-RNN Models on Multimodal Physiological Signals.,” AlKadhim Journal for Computer Science, vol. 3, no. 2, pp. 20–29, Jun. 2025, doi: 10.61710/kjcs.v3i2.100.

S. Indolia, A. K. Goswami, S. P. Mishra, and P. Asopa, “Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach,” in Procedia Computer Science, Elsevier B.V., 2018, pp. 679–688. doi: 10.1016/j.procs.2018.05.069.

A. Vaswani et al., “Attention Is All You Need,” 2023.

Z. Wang, D. Huang, J. Cui, X. Zhang, S. B. Ho, and E. Cambria, “A review of Chinese sentiment analysis: subjects, methods, and trends,” Artif Intell Rev, vol. 58, no. 3, Mar. 2025, doi: 10.1007/s10462-024-10988-9.

J. Yang, X. Huang, H. Wu, and X. Yang, “EEG-based emotion classification based on Bidirectional Long Short-Term Memory Network,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 491–504. doi: 10.1016/j.procs.2020.06.117.