van den Corput, D. Locked in Syndrome Machine Studying Classification Utilizing Sentence Comprehension EEG Information. (2020).
Ramsey, N. F. & Crone, N. E. Mind implants that allow speech cross efficiency milestones. Nature 620, 7976. (2023).
Lee, J. S., Jo, H. N. & Lee, S. H. In the direction of Unified Neural Decoding of Perceived, Spoken and Imagined Speech from EEG Indicators. (2024).
Ramirez Campos, M. S. et al. A machine studying strategy to classifying EEG information collected with or with out haptic suggestions throughout a simulated drilling job. Mind Sci. 14 (9), 894. (2024).
Zhang, Z. et al. An EEG-based BCI dataset for decoding of imagined speech. Sci. Information. 11, 1265. (2024).
Siddhad, G., Roy, P. P. & Kim, B. G. Neural networks meet neural exercise: Using EEG for psychological workload estimation. In Pattern Recognition. ICPR 2024. Lecture Notes in Pc Science (eds Antonacopoulos, A. et al.) 325–339. (Springer, 2025).
Bagheri, I., Alizadeh, S., Ghazavi Khorasgani, M. M. & Asgharighajari, M. A scientific investigation based mostly on BCI and EEG carried out utilizing machine studying algorithms. Int. J. Mod. Achiev. Sci. Eng. Technol. 45, 1–15. (2024).
Demirezen, G., Taşkaya Temizel, T. & Brouwer, A-M. Reproducible machine studying analysis in psychological workload classification utilizing EEG. Entrance. Neuroergon. 5, 1346794. (2024).
Lee, J. et al. Towards Absolutely-Finish-to-Finish Listened Speech Decoding from EEG Indicators. (2024).
Anderson, A. J. & Perone, S. Developmental change within the resting state electroencephalogram: Insights into cognition and the mind. Mind Cogn. 126, 40–52. (2018).
Chaddad, A., Wu, Y., Kateb, R. & Bouridane, A. Electroencephalography sign processing: a complete overview and evaluation of strategies and strategies. Sensors 23 (14), 6434. (2023).
Sweeney, Ok. T., Ward, T. E. & McLoone, S. F. Artifact elimination in physiological indicators–practices and prospects. IEEE Trans. Inf. Technol. Biomed. 16 (3), 488–500. (2012).
Cui, S., Lee, D. & Wen, D. Towards brain-inspired basis mannequin for EEG sign processing: our opinion. Entrance. Neurosci. 18, 1507654. (2024).
Solar, C. & Mou, C. Survey on the analysis course of EEG-based sign processing. Entrance. Neurosci. 17, 1203059. (2023).
Jammisetty, Y. et al. Cognitive load detection by means of EEG lead sensible characteristic optimization and ensemble classification. Sci. Rep. 15, 842. (2025).
Carvalho, V. R. et al. Decoding imagined speech with delay differential evaluation. Entrance. Hum. Neurosci. 18, 1398065. (2024).
Kamble, A., Ghare, P. H., Kumar, V., Kothari, A. & Keskar, A. G. Spectral evaluation of EEG indicators for computerized imagined speech recognition. IEEE Trans. Instrum. Meas. 72, 4009409. (2023).
Tripathi, A. Evaluation of EEG frequency bands for envisioned speech recognition. (2022).
Lopez-Bernal, D., Balderas, D., Ponce, P. & Molina, A. A state-of-the-art overview of EEG-based imagined speech decoding. Entrance. Hum. Neurosci. 16, 867281. (2022).
Bozhkov, L. & Georgieva, P. Overview of deep studying architectures for EEG-based mind imaging. In 2018 Worldwide Joint Convention on Neural Networks (IJCNN), Rio de Janeiro, Brazil, 1–7. (2018).
Saha, P. & Fels, S. Hierarchical deep characteristic studying for decoding imagined speech from EEG. In Proceedings of the AAAI Convention on Synthetic Intelligence; 2019 Jul 8-13; Honolulu, HI, USA, 10019–10020. (2019).
Lee, D. H., Kim, S. J. & Lee, Ok. W. Decoding high-level imagined speech utilizing attention-based deep neural networks. In 2022 tenth Worldwide Winter Convention on Mind-Pc Interface (BCI); 2022; Gangwon-do, Republic of Korea, 1–7. (IEEE, 2022).
Lee, Y. E. & Lee, S. H. EEG-transformer: self-attention from transformer structure for decoding EEG of imagined speech. In 2022 tenth Worldwide Winter Convention on Mind-Pc Interface (BCI); 2022 Feb 21–23; Gangwon-do, Republic of Korea, 1–7. (IEEE, 2022).
Gallo, I. & Corchs, S. E. Considering is like processing a sequence of spatial and temporal phrases. In 2024 Worldwide Joint Convention on Neural Networks (IJCNN) (IEEE, 2024).
García-Salinas, J. S., Villaseñor-Pineda, L., Reyes-García, C. A. & Torres-García, A. A. Switch studying in imagined speech EEG-based BCIs. Biomed. Sign. Course of. Management. 50, 151–157. (2019).
Kumar, P., Saini, R., Roy, P. P., Sahu, P. Ok. & Dogra, D. P. Envisioned speech recognition utilizing EEG sensors. Pers. Ubiquit Comput. 22 (2), 185–199. (2018).
Alzahrani, S., Banjar, H. & Mirza, R. Systematic overview of EEG-based imagined speech classification strategies. Sensors 24 (24), 8168. (2024).
Park, H. & Lee, B. Multiclass classification of imagined speech EEG utilizing noise-assisted multivariate empirical mode decomposition and multireceptive subject convolutional neural community. Entrance. Hum. Neurosci. 17, 1186594. (2023).
Zhang, W., Tang, X. & Wang, M. Consideration mannequin of EEG indicators based mostly on reinforcement studying. Entrance. Hum. Neurosci. 18, 1442398. (2024).
Lee, Y. E., Lee, S. H., Kim, S. H. & Lee, S. W. In the direction of Voice Reconstruction from EEG throughout Imagined Speech. In: Proceedings of the AAAI Convention on Synthetic Intelligence; ;37(5):6030–6038. (2023). https://doi.org/10.1609/aaai.v37i5.25745
Kim, S., Lee, Y. E., Lee, S. H. & Lee, S. W. Diff-E: Diffusion-based studying for decoding imagined speech EEG. In INTERSPEECH 2023, 1159–1163 (ISCA, 2023).
Mallick, S. & Baths, V. Novel deep studying framework for detection of epileptic seizures utilizing EEG indicators. Entrance. Comput. Neurosci. 18, 1340251. (2024).
Abedinzadeh Torghabeh, F., Hosseini, S. A. & Ahmadi Moghadam, E. Enhancing Parkinson’s illness severity evaluation by means of voice-based wavelet scattering, optimized mannequin choice, and weighted majority voting. Med. Novel Technol. Gadgets. 20, 100266. (2023).
Abdulghani, M. M., Walters, W. L. & Abed, Ok. H. Imagined speech classification utilizing EEG and deep studying. Bioengineering 10 (6), 649. (2023).
Alharbi, Y. F. & Alotaibi, Y. A. Decoding imagined speech from EEG information: a hybrid deep studying strategy to capturing spatial and temporal options. Life 14 (11), 1501. (2024).
BCI Competitors Committee. Worldwide BCI Competitors [dataset]. OSF; 2022. (2020).
Modaresnia, Y., Abedinzadeh Torghabeh, F. & Hosseini, S. A. Enhancing multi-class diabetic retinopathy detection utilizing tuned hyper-parameters and modified deep switch studying. Multimed Instruments Appl. 83, 81455–81476. (2024).
Tirupattur, P., Rawat, Y. S., Spampinato, C. & Shah, M. ThoughtViz visualizing human ideas utilizing generative adversarial community. In ACM Multimedia Convention, 4. (ACM, 2018).
Kumar, P. & Scheme, E. A. Deep spatio-temporal mannequin for EEG-based imagined speech recognition. In 2021 IEEE Worldwide Convention on Acoustics, Speech and Sign Processing (ICASSP) (IEEE, 2021).



