Research progress in electroencephalogram-based brain age prediction_Journal of Biomedical Engineering

Authors：

ZU Hongyue ^1,2 , ZHAN Ping ^1,2 , YU Hui ^1,2 ,  WANG Weidong ^1,2 ,  LIU Hongyun ^1,2

1. Medical Innovation & Research Division, Chinese PLA General Hospital, Beijing 100853, P. R. China;
2. Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Beijing 100853, P. R. China;

Corresponding author：

Keywords：

Electroencephalogram; Brain age prediction; Machine learning; Deep learning; Clinical application

DOI：

10.7507/1001-5515.202503043

Video：

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Abstract Full text Figures/Tables Video References Cited by

Brain age prediction, as a significant approach for assessing brain health and early diagnosing neurodegenerative diseases, has garnered widespread attention in recent years. Electroencephalogram (EEG), an non-invasive, convenient, and cost-effective neurophysiological signal, offers unique advantages for brain age prediction due to its high temporal resolution and strong correlation with brain functional states. Despite substantial progress in enhancing prediction accuracy and generalizability, challenges remain in data quality and model interpretability. This review comprehensively examined the advancements in EEG-based brain age prediction, detailing key aspects of data preprocessing, feature extraction, model construction, and result evaluation. It also summarized the current applications of machine learning and deep learning methods in this field, analyzed existing issues, and explored future directions to promote the widespread application of EEG-based brain age prediction in both clinical and research settings.

Citation： ZU Hongyue, ZHAN Ping, YU Hui, WANG Weidong, LIU Hongyun. Research progress in electroencephalogram-based brain age prediction. Journal of Biomedical Engineering, 2025, 42(4): 832-840. doi: 10.7507/1001-5515.202503043 Copy

1.	Salih A, Boscolo Galazzo I, Raisi-Estabragh Z, et al. Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants. Sci Rep, 2021, 11(1): 20563.
2.	Mishra S, Beheshti I, Khanna P. A review of neuroimaging-driven brain age estimation for identification of brain disorders and health conditions. IEEE Rev Biomed Eng, 2023, 16: 371-385.
3.	Seitz-Holland J, Haas S S, Penzel N, et al. Brain AGE, brain health, and mental disorders: A systematic review. Neurosci Biobehav Rev, 2024, 159: 105581.
4.	Soumya Kumari L K, Sundarrajan R. A review on brain age prediction models. Brain Res, 2024, 1823: 148668.
5.	Wen J, Zhao B, Yang Z, et al. The genetic architecture of multimodal human brain age. Nat Commun, 2024, 15(1): 2604.
6.	Wu Y, Gao H, Zhang C, et al. Machine learning and deep learning approaches in lifespan brain age prediction: A comprehensive review. Tomography, 2024, 10(8): 1238-1262.
7.	Holm M C, Leonardsen E H, Beck D, et al. Linking brain maturation and puberty during early adolescence using longitudinal brain age prediction in the ABCD cohort. Dev Cogn Neurosci, 2023, 60: 101220.
8.	Javaid H, Kumarnsit E, Chatpun S. Age-related alterations in EEG network connectivity in healthy aging. Brain Sci, 2022, 12(2): 218.
9.	Wang J, Fang J, Xu Y, et al. Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder. Front Hum Neurosci, 2022, 16: 1074587.
10.	Dong X, Kong Y, Xu Y, et al. Development and validation of Auto-Neo-electroencephalography (EEG) to estimate brain age and predict report conclusion for electroencephalography monitoring data in neonatal intensive care units. Ann Transl Med, 2021, 9(16): 1290.
11.	Bomatter P, Paillard J, Garces P, et al. Machine learning of brain-specific biomarkers from EEG. EbioMedicine, 2024, 106: 105259.
12.	Al Fahoum A, Zyout A. Wavelet transform, reconstructed phase space, and deep learning neural networks for EEG-based schizophrenia detection. Int J Neural Syst, 2024, 34(9): 2450046.
13.	Jiao Y, Zheng Q, Qiao D, et al. EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI. Biol Cybern, 2024, 118(1-2): 21-37.
14.	Swamy C P, Besheli B F, Branco L R F, et al. Pulsation artifact removal from intra-operatively recorded local field potentials using sparse signal processing and data-specific dictionary. Annu Int Conf IEEE Eng Med Biol Soc, 2023, 2023: 1-4.
15.	Yedukondalu J, Sharma L D. Circulant singular spectrum analysis and discrete wavelet transform for automated removal of EOG artifacts from EEG signals. Sensors (Basel), 2023, 23(3): 1235.
16.	He P, Wilson G, Russell C. Removal of ocular artifacts from electro-encephalogram by adaptive filtering. Med Biol Eng Comput, 2004, 42(3): 407-412.
17.	Sun H, Jia J, Goparaju B, et al. Large-scale automated sleep staging. Sleep, 2017, 40(10): zsx139.
18.	Yook S, Park H R, Joo E Y, et al. Predicting the impact of CPAP on brain health: A study using the sleep EEG-derived brain age index. Ann Clin Transl Neurol, 2024, 11(5): 1172-1183.
19.	Apicella A, Isgrò F, Pollastro A, et al. On the effects of data normalization for domain adaptation on EEG data. Eng Appl Artif Intell, 2023, 123: 106205.
20.	Iyer K K, Roberts J A, Waak M, et al. Optimization of time series features to estimate brain age in children from electroencephalography. Annu Int Conf IEEE Eng Med Biol Soc, 2023, 2023: 1-4.
21.	Geng X, Wang L, Yu P, et al. A method of EEG signal feature extraction based on hybrid DWT and EMD. Alex Eng J, 2025, 113: 195-204.
22.	Saghab Torbati M, Zandbagleh A, Daliri M R, et al. Explainable AI for bipolar disorder diagnosis using Hjorth parameters. Diagnostics (Basel), 2025, 15(3): 316.
23.	Kaushik G, Gaur P, Sharma R R, et al. EEG signal based seizure detection focused on Hjorth parameters from tunable-Q wavelet sub-bands. Biomedical Signal Processing and Control, 2022, 76: 103645.
24.	Monsy J C, Vinod A P. EEG-based biometric identification using frequency-weighted power feature. IET Biom, 2020, 9(6): 251-258.
25.	Shen M, Yang F, Wen P, et al. A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network. Heliyon, 2024, 10(11): e31827.
26.	Morales S, Bowers M E. Time-frequency analysis methods and their application in developmental EEG data. Dev Cognit Neurosci, 2022, 54: 101067.
27.	Cai G, Zhang F, Yang B, et al. Manifold learning-based common spatial pattern for EEG signal classification. IEEE J Biomed Health Inform, 2024, 28(4): 1971-1981.
28.	Reddy T K, Arora V, Behera L, et al. Multiclass fuzzy time-delay common spatio-spectral patterns with fuzzy information theoretic optimization for EEG-based regression problems in brain-computer interface (BCI). IEEE T Fuzzy Syst, 2019, 27(10): 1943-1951.
29.	Simons S, Espino P, Abásolo D. Fuzzy entropy analysis of the electroencephalogram in patients with Alzheimer’s disease: Is the method superior to sample entropy? Entropy (Basel), 2018, 20(1): 21.
30.	Namazi H, Kulish V V, Hussaini J, et al. A signal processing based analysis and prediction of seizure onset in patients with epilepsy. Oncotarget, 2016, 7(1): 342-350.
31.	Sun H, Paixao L, Oliva J T, et al. Brain age from the electroencephalogram of sleep. Neurobiol Aging, 2019, 74: 112-120.
32.	Ye E, Sun H, Leone M J, et al. Association of sleep electroencephalography-based brain age index with dementia. JAMA Netw Open, 2020, 3(9): e2017357.
33.	Li L, Li J, Wu H, et al. Optimal channel and feature selection for automatic prediction of functional brain age of preterm infant based on EEG. Front Neurosci, 2025, 19: 1517141.
34.	Stevenson N J, Oberdorfer L, Koolen N, et al. Functional maturation in preterm infants measured by serial recording of cortical activity. Sci Rep, 2017, 7(1): 12969.
35.	Zandvoort C S, van der Vaart M, Robinson S, et al. Sensory event-related potential morphology predicts age in premature infants. Clin Neurophysiol, 2024, 157: 61-72.
36.	Stevenson N J, Nordvik T, Espeland C N, et al. Inter-site generalizability of EEG based age prediction algorithms in the preterm infant. Physiol Meas, 2023, 44(7): 10.
37.	Paixao L, Sikka P, Sun H, et al. Excess brain age in the sleep electroencephalogram predicts reduced life expectancy. Neurobiol Aging, 2020, 88: 150-155.
38.	Banville H, Jaoude M A, Wood S, et al. Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG. Imaging Neuroscience, 2024, 2: 1-15.
39.	Lavanga M, De Wel O, Caicedo A, et al. A brain-age model for preterm infants based on functional connectivity. Physiol Meas, 2018, 39(4): 044006.
40.	Vandenbosch M M L J Z, van 't Ent D, Boomsma D I, et al. EEG-based age-prediction models as stable and heritable indicators of brain maturational level in children and adolescents. Hum Brain Mapp, 2019, 40(6): 1919-1926.
41.	Obada A Z, Chung K W, Kuplicki R T, et al. Predicting age from brain EEG signals—A machine learning approach. Front Aging Neurosci, 2018, 10: 184.
42.	Kounios J, Fleck J I, Zhang F, et al. Brain-age estimation with a low-cost EEG-headset: effectiveness and implications for large-scale screening and brain optimization. Front Neuroergon, 2024, 5: 1340732.
43.	Ansari A, Pillay K, Arasteh E, et al. Resting state electroencephalographic brain activity in neonates can predict age and is indicative of neurodevelopmental outcome. Clin Neurophysiol, 2024, 163: 226-235.
44.	Paliwal V, Das K, Doesburg S M, et al. Classifying routine clinical electroencephalograms with multivariate iterative filtering and convolutional neural networks. IEEE Trans Neural Syst Rehabil Eng, 2024, 32: 2038-2048.
45.	Khayretdinova M, Shovkun A, Degtyarev V, et al. Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset. Front Aging Neurosci, 2022, 14: 1019869.
46.	Gschwandtner L, Hartmann M, Oberdorfer L, et al. Deep learning for estimation of functional brain maturation from EEG of premature neonates. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 2020: 104-107.
47.	Yook S, Miao Y, Park C, et al. Predicting brain age based on sleep EEG and DenseNet. Annu Int Conf IEEE Eng Med Biol Soc, 2021, 2021: 245-248.
48.	Yook S, Park H R, Park C, et al. Novel neuroelectrophysiological age index associated with imaging features of brain aging and sleep disorders. Neuroimage, 2022, 264: 119753.
49.	Jusseaume K, Valova I. Brain age prediction/classification through recurrent deep learning with electroencephalogram recordings of seizure subjects. Sensors, 2022, 22(21): 8112.
50.	Wong S B, Tsao Y, Tsai W H, et al. Application of bidirectional long short-term memory network for prediction of cognitive age. Sci Rep, 2023, 13(1): 20197.
51.	Kaushik P, Gupta A, Roy P P, et al. EEG-based age and gender prediction using deep BLSTM-LSTM network model. IEEE Sens J, 2019, 19(7): 2634-2641.
52.	Zhang D, She Y, Sun J, et al. Brain age estimation from overnight sleep electroencephalography with multi-flow sequence learning. Nat Sci Sleep, 2024, 16: 879-896.
53.	Brink-Kjaer A, Leary E B, Sun H, et al. Age estimation from sleep studies using deep learning predicts life expectancy. NPJ Digit Med, 2022, 5(1): 103.
54.	Moguilner S, Baez S, Hernandez H, et al. Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations. Nat Med, 2024, 30(12): 3646-3657.
55.	关舒月, 吕奕谋, 李永春, 等. 基于面部视频的非接触式心率测量的深度学习方法综述. 生物医学工程学杂志, 2025, 42(1): 197-204.
56.	An Y, Lam H K, Ling S H. Auto-denoising for EEG signals using generative adversarial network. Sensors (Basel), 2022, 22(5): 1750.
57.	Rajpura P, Cecotti H, Kumar Meena Y. Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space. J Neural Eng, 2024, 21(4): 041003.
58.	Vimbi V, Shaffi N, Mahmud M. Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer’s disease detection. Brain Inform, 2024, 11(1): 10.
59.	Sarvi Zargar B, Karami Mollaei M R, Ebrahimi F, et al. Generalizable epileptic seizures prediction based on deep transfer learning. Cogn Neurodyn, 2023, 17(1): 119-131.
60.	Sedgwick R, Goertz J P, Stevens M M, et al. Transfer learning Bayesian optimization for competitor DNA molecule design for use in diagnostic assays. Biotechnol Bioeng. 2025, 122(1): 189-210.

1. Salih A, Boscolo Galazzo I, Raisi-Estabragh Z, et al. Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants. Sci Rep, 2021, 11(1): 20563.
2. Mishra S, Beheshti I, Khanna P. A review of neuroimaging-driven brain age estimation for identification of brain disorders and health conditions. IEEE Rev Biomed Eng, 2023, 16: 371-385.
3. Seitz-Holland J, Haas S S, Penzel N, et al. Brain AGE, brain health, and mental disorders: A systematic review. Neurosci Biobehav Rev, 2024, 159: 105581.
4. Soumya Kumari L K, Sundarrajan R. A review on brain age prediction models. Brain Res, 2024, 1823: 148668.
5. Wen J, Zhao B, Yang Z, et al. The genetic architecture of multimodal human brain age. Nat Commun, 2024, 15(1): 2604.
6. Wu Y, Gao H, Zhang C, et al. Machine learning and deep learning approaches in lifespan brain age prediction: A comprehensive review. Tomography, 2024, 10(8): 1238-1262.
7. Holm M C, Leonardsen E H, Beck D, et al. Linking brain maturation and puberty during early adolescence using longitudinal brain age prediction in the ABCD cohort. Dev Cogn Neurosci, 2023, 60: 101220.
8. Javaid H, Kumarnsit E, Chatpun S. Age-related alterations in EEG network connectivity in healthy aging. Brain Sci, 2022, 12(2): 218.
9. Wang J, Fang J, Xu Y, et al. Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder. Front Hum Neurosci, 2022, 16: 1074587.
10. Dong X, Kong Y, Xu Y, et al. Development and validation of Auto-Neo-electroencephalography (EEG) to estimate brain age and predict report conclusion for electroencephalography monitoring data in neonatal intensive care units. Ann Transl Med, 2021, 9(16): 1290.
11. Bomatter P, Paillard J, Garces P, et al. Machine learning of brain-specific biomarkers from EEG. EbioMedicine, 2024, 106: 105259.
12. Al Fahoum A, Zyout A. Wavelet transform, reconstructed phase space, and deep learning neural networks for EEG-based schizophrenia detection. Int J Neural Syst, 2024, 34(9): 2450046.
13. Jiao Y, Zheng Q, Qiao D, et al. EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI. Biol Cybern, 2024, 118(1-2): 21-37.
14. Swamy C P, Besheli B F, Branco L R F, et al. Pulsation artifact removal from intra-operatively recorded local field potentials using sparse signal processing and data-specific dictionary. Annu Int Conf IEEE Eng Med Biol Soc, 2023, 2023: 1-4.
15. Yedukondalu J, Sharma L D. Circulant singular spectrum analysis and discrete wavelet transform for automated removal of EOG artifacts from EEG signals. Sensors (Basel), 2023, 23(3): 1235.
16. He P, Wilson G, Russell C. Removal of ocular artifacts from electro-encephalogram by adaptive filtering. Med Biol Eng Comput, 2004, 42(3): 407-412.
17. Sun H, Jia J, Goparaju B, et al. Large-scale automated sleep staging. Sleep, 2017, 40(10): zsx139.
18. Yook S, Park H R, Joo E Y, et al. Predicting the impact of CPAP on brain health: A study using the sleep EEG-derived brain age index. Ann Clin Transl Neurol, 2024, 11(5): 1172-1183.
19. Apicella A, Isgrò F, Pollastro A, et al. On the effects of data normalization for domain adaptation on EEG data. Eng Appl Artif Intell, 2023, 123: 106205.
20. Iyer K K, Roberts J A, Waak M, et al. Optimization of time series features to estimate brain age in children from electroencephalography. Annu Int Conf IEEE Eng Med Biol Soc, 2023, 2023: 1-4.
21. Geng X, Wang L, Yu P, et al. A method of EEG signal feature extraction based on hybrid DWT and EMD. Alex Eng J, 2025, 113: 195-204.
22. Saghab Torbati M, Zandbagleh A, Daliri M R, et al. Explainable AI for bipolar disorder diagnosis using Hjorth parameters. Diagnostics (Basel), 2025, 15(3): 316.
23. Kaushik G, Gaur P, Sharma R R, et al. EEG signal based seizure detection focused on Hjorth parameters from tunable-Q wavelet sub-bands. Biomedical Signal Processing and Control, 2022, 76: 103645.
24. Monsy J C, Vinod A P. EEG-based biometric identification using frequency-weighted power feature. IET Biom, 2020, 9(6): 251-258.
25. Shen M, Yang F, Wen P, et al. A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network. Heliyon, 2024, 10(11): e31827.
26. Morales S, Bowers M E. Time-frequency analysis methods and their application in developmental EEG data. Dev Cognit Neurosci, 2022, 54: 101067.
27. Cai G, Zhang F, Yang B, et al. Manifold learning-based common spatial pattern for EEG signal classification. IEEE J Biomed Health Inform, 2024, 28(4): 1971-1981.
28. Reddy T K, Arora V, Behera L, et al. Multiclass fuzzy time-delay common spatio-spectral patterns with fuzzy information theoretic optimization for EEG-based regression problems in brain-computer interface (BCI). IEEE T Fuzzy Syst, 2019, 27(10): 1943-1951.
29. Simons S, Espino P, Abásolo D. Fuzzy entropy analysis of the electroencephalogram in patients with Alzheimer’s disease: Is the method superior to sample entropy? Entropy (Basel), 2018, 20(1): 21.
30. Namazi H, Kulish V V, Hussaini J, et al. A signal processing based analysis and prediction of seizure onset in patients with epilepsy. Oncotarget, 2016, 7(1): 342-350.
31. Sun H, Paixao L, Oliva J T, et al. Brain age from the electroencephalogram of sleep. Neurobiol Aging, 2019, 74: 112-120.
32. Ye E, Sun H, Leone M J, et al. Association of sleep electroencephalography-based brain age index with dementia. JAMA Netw Open, 2020, 3(9): e2017357.
33. Li L, Li J, Wu H, et al. Optimal channel and feature selection for automatic prediction of functional brain age of preterm infant based on EEG. Front Neurosci, 2025, 19: 1517141.
34. Stevenson N J, Oberdorfer L, Koolen N, et al. Functional maturation in preterm infants measured by serial recording of cortical activity. Sci Rep, 2017, 7(1): 12969.
35. Zandvoort C S, van der Vaart M, Robinson S, et al. Sensory event-related potential morphology predicts age in premature infants. Clin Neurophysiol, 2024, 157: 61-72.
36. Stevenson N J, Nordvik T, Espeland C N, et al. Inter-site generalizability of EEG based age prediction algorithms in the preterm infant. Physiol Meas, 2023, 44(7): 10.
37. Paixao L, Sikka P, Sun H, et al. Excess brain age in the sleep electroencephalogram predicts reduced life expectancy. Neurobiol Aging, 2020, 88: 150-155.
38. Banville H, Jaoude M A, Wood S, et al. Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG. Imaging Neuroscience, 2024, 2: 1-15.
39. Lavanga M, De Wel O, Caicedo A, et al. A brain-age model for preterm infants based on functional connectivity. Physiol Meas, 2018, 39(4): 044006.
40. Vandenbosch M M L J Z, van 't Ent D, Boomsma D I, et al. EEG-based age-prediction models as stable and heritable indicators of brain maturational level in children and adolescents. Hum Brain Mapp, 2019, 40(6): 1919-1926.
41. Obada A Z, Chung K W, Kuplicki R T, et al. Predicting age from brain EEG signals—A machine learning approach. Front Aging Neurosci, 2018, 10: 184.
42. Kounios J, Fleck J I, Zhang F, et al. Brain-age estimation with a low-cost EEG-headset: effectiveness and implications for large-scale screening and brain optimization. Front Neuroergon, 2024, 5: 1340732.
43. Ansari A, Pillay K, Arasteh E, et al. Resting state electroencephalographic brain activity in neonates can predict age and is indicative of neurodevelopmental outcome. Clin Neurophysiol, 2024, 163: 226-235.
44. Paliwal V, Das K, Doesburg S M, et al. Classifying routine clinical electroencephalograms with multivariate iterative filtering and convolutional neural networks. IEEE Trans Neural Syst Rehabil Eng, 2024, 32: 2038-2048.
45. Khayretdinova M, Shovkun A, Degtyarev V, et al. Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset. Front Aging Neurosci, 2022, 14: 1019869.
46. Gschwandtner L, Hartmann M, Oberdorfer L, et al. Deep learning for estimation of functional brain maturation from EEG of premature neonates. Annu Int Conf IEEE Eng Med Biol Soc, 2020, 2020: 104-107.
47. Yook S, Miao Y, Park C, et al. Predicting brain age based on sleep EEG and DenseNet. Annu Int Conf IEEE Eng Med Biol Soc, 2021, 2021: 245-248.
48. Yook S, Park H R, Park C, et al. Novel neuroelectrophysiological age index associated with imaging features of brain aging and sleep disorders. Neuroimage, 2022, 264: 119753.
49. Jusseaume K, Valova I. Brain age prediction/classification through recurrent deep learning with electroencephalogram recordings of seizure subjects. Sensors, 2022, 22(21): 8112.
50. Wong S B, Tsao Y, Tsai W H, et al. Application of bidirectional long short-term memory network for prediction of cognitive age. Sci Rep, 2023, 13(1): 20197.
51. Kaushik P, Gupta A, Roy P P, et al. EEG-based age and gender prediction using deep BLSTM-LSTM network model. IEEE Sens J, 2019, 19(7): 2634-2641.
52. Zhang D, She Y, Sun J, et al. Brain age estimation from overnight sleep electroencephalography with multi-flow sequence learning. Nat Sci Sleep, 2024, 16: 879-896.
53. Brink-Kjaer A, Leary E B, Sun H, et al. Age estimation from sleep studies using deep learning predicts life expectancy. NPJ Digit Med, 2022, 5(1): 103.
54. Moguilner S, Baez S, Hernandez H, et al. Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations. Nat Med, 2024, 30(12): 3646-3657.
55. 关舒月, 吕奕谋, 李永春, 等. 基于面部视频的非接触式心率测量的深度学习方法综述. 生物医学工程学杂志, 2025, 42(1): 197-204.
56. An Y, Lam H K, Ling S H. Auto-denoising for EEG signals using generative adversarial network. Sensors (Basel), 2022, 22(5): 1750.
57. Rajpura P, Cecotti H, Kumar Meena Y. Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space. J Neural Eng, 2024, 21(4): 041003.
58. Vimbi V, Shaffi N, Mahmud M. Interpreting artificial intelligence models: a systematic review on the application of LIME and SHAP in Alzheimer’s disease detection. Brain Inform, 2024, 11(1): 10.
59. Sarvi Zargar B, Karami Mollaei M R, Ebrahimi F, et al. Generalizable epileptic seizures prediction based on deep transfer learning. Cogn Neurodyn, 2023, 17(1): 119-131.
60. Sedgwick R, Goertz J P, Stevens M M, et al. Transfer learning Bayesian optimization for competitor DNA molecule design for use in diagnostic assays. Biotechnol Bioeng. 2025, 122(1): 189-210.

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