Machine Learning in Cognitive Enhancement: A Systematic Review

Wang Ziyu; Zhang Ziyuan; Zhu Rongjuan; You Xuqun; Liang Jimin

doi:10.16719/j.cnki.1671-6981.20240623

PDF(1299 KB)

Journal of Psychological Science ›› 2024, Vol. 47 ›› Issue (6) : 1519-1529. DOI: 10.16719/j.cnki.1671-6981.20240623

Theories & History of Psychology

Machine Learning in Cognitive Enhancement: A Systematic Review

Wang Ziyu^1,2,3, Zhang Ziyuan², Zhu Rongjuan⁴, You Xuqun³, Liang Jimin²

Author information +

History +

Abstract

Cognitive ability refers to the capacity to process, store, and retrieve information, and it is a strong predictor of short- and long-term achievements such as academic performance, social skills, health, wealth, and involvement in criminal activities. Common cognitive enhancement methods include cognitive training, neurofeedback, and electrical stimulation. However, these methods have several shortcomings, including long training sessions, fixed training parameters, and inconsistent training outcomes. Recently, machine learning has provided an opportunity to obtain more intelligent, personalized, and precise cognitive training solutions, which can overcome the shortcomings of previous methods and maximize training effectiveness. Therefore, this study first introduced common cognitive enhancement and machine learning methods. Then, the relationship between machine learning and cognitive enhancement was analyzed, and a comprehensive overview of the application of machine learning in cognitive enhancement was provided. Finally, the challenges and potential directions for future research were discussed.
Cognitive training is the most common cognitive enhancement method that can effectively improve the performance on training tasks and structurally similar untrained tasks. Meanwhile, successful cognitive training may potentially enhance the performance on transfer tasks and show maintenance effects (i.e., cognitive control, reasoning, intelligence, and cross-modal tasks). However, cognitive training can be time-consuming and has inconsistent effectiveness. Neurofeedback enables individuals to self-regulate their brain activity through signals such as electroencephalography (EEG), magnetic resonance imaging (MRI), and near-infrared spectroscopy (NIRS), and can significantly improve attention, working memory, and cognitive control. However, neurofeedback equipment is expensive, training parameters may be overly simplified, and the training process is affected by emotions. Electrical stimulation techniques are used to activate or inhibit specific brain regions, such as the dorsolateral prefrontal cortex. Studies have shown that both single and repetitive stimulation can improve executive control, attention, and multitasking abilities. Moreover, the combination of electrical stimulation with cognitive training can further improve training outcomes. However, the underlying mechanism is still unclear, and problems such as fixed parameters and inconsistent training effects remain.
Supervised machine learning is extensively used in the fields of neuroscience and psychology. Specifically, algorithms and labeled datasets are used to train machine learning models that can recognize patterns, and then these models are used to predict labels for new data. Supervised machine learning can be divided into regression algorithms, classification algorithms, and deep learning. Specifically, regression algorithms predict the scores of psychological traits by outputting continuous values, including linear regression, stepwise regression, lasso regression, ridge regression, elastic net regression, and support vector regression. For classification tasks such as determining whether an individual has a psychological disorder, algorithms like logistic regression, linear discriminant analysis, k-nearest neighbors, support vector machines, decision trees, and random forests are adopted. Additionally, deep learning models such as convolutional neural networks, recurrent neural networks, and transformer networks are particularly suitable for complex cognitive tasks because of their ability to automatically learn feature representations.
There is a mutually beneficial relationship between machine learning and cognitive enhancement. On the one hand, machine learning helps to overcome the limitations of cognitive enhancement methods and maximize the effectiveness of training. Specifically, machine learning can assist in the selection of difficulty levels and training programs before the training. Meanwhile, machine learning can be employed to adjust the task difficulty and electrical stimulation parameters during the training process. Another scenario involving machine learning during the training process is neurofeedback decoding, where machine learning is utilized to decode task-related brain activity patterns, which are then used as indicators for implementing precise real-time neurofeedback. As for training effects, machine learning can be adopted to analyze the relationship between brain data and cognitive abilities, which helps to reduce the number of assessment tasks. On the other hand, the integration and enhancement of brain signals can improve the performance of machine-learning models. This brings a new meaning to cognitive training from the perspective of machine learning.
It is noteworthy that related research is still in the exploratory stage and faces several challenges. First, in terms of training evaluation, existing cognitive prediction models have relatively low accuracy, and this issue can be addressed in future research through algorithm selection, multimodal fusion, feature selection, and hyperparameter tuning. Second, sample diversity is crucial for personalized cognitive training and assessment. Future research should focus on increasing the sample size and diversity of publicly available datasets. Third, model interpretability can help researchers to understand the mechanisms underlying cognitive training. Therefore, future research should flexibly apply interpretability methods in traditional machine learning models and develop interpretable deep learning methods to ensure the interpretability of deep learning models.

Key words

cognitive enhancement / machine learning / brain-computer fusion

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Wang Ziyu, Zhang Ziyuan, Zhu Rongjuan, You Xuqun, Liang Jimin. Machine Learning in Cognitive Enhancement: A Systematic Review[J]. Journal of Psychological Science. 2024, 47(6): 1519-1529 https://doi.org/10.16719/j.cnki.1671-6981.20240623

References

[1] 董健宇, 韦文棋, 吴珂, 妮娜, 王粲霏, 付莹, 彭歆. (2020). 机器学习在抑郁症领域的应用. 心理科学进展, 28(2), 266-274.
[2] 孙鑫, 黎坚, 符植煜. (2018). 利用游戏log-file预测学生推理能力和数学成绩——机器学习的应用. 心理学报, 50(7), 761-770.
[3] 王钰莹, 于海峰, 黎兵, 鲁学明. (2022). 如何利用开源工具分析fMRI数据: 基于多体素模式的有监督机器学习算法介绍. 心理科学, 45(3), 718-724.
[4] 王梓宇, 孔子叶, 朱荣娟, 游旭群. (2019). 任务转换训练和执行功能可塑性. 心理科学进展, 27(10), 1667-1676.
[5] 游旭群, 杨畅, 罗扬眉. (2019). 基于非侵入性脑刺激的认知增强: 方法、伦理和应用. 心理科学, 42(4), 813-819.
[6] 赵鑫, 李红利. (2022). 工作记忆训练: 研究现状和趋势. 西北师大学报(社会科学版), 59(6), 173-184.
[7] Abrol A., Fu Z. N., Salman M., Silva R., Du Y. H., Plis S., & Calhoun V. (2021). Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning. Nature Communications, 12(1), Article 353.
[8] Albizu A., Indahlastari A., Huang Z. Q., Waner J., Stolte S. E., Fang R. G., & Woods A. J. (2023). Machine-learning defined precision tDCS for improving cognitive function. Brain Stimulation, 16(3), 969-974.
[9] Cantin R. H., Gnaedinger E. K., Gallaway K. C., Hesson-McInnis M. S., & Hund A. M. (2016). Executive functioning predicts reading, mathematics, and theory of mind during the elementary years. Journal of Experimental Child Psychology, 146, 66-78.
[10] Da Silva, J. C., & De Souza, M. L. (2021). Neurofeedback training for cognitive performance improvement in healthy subjects: A systematic review. Psychology and Neuroscience, 14(3), 262-279.
[11] Dhamala E., Jamison K. W., Jaywant A., Dennis S., & Kuceyeski A. (2021). Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults. Human Brain Mapping, 42(10), 3102-3118.
[12] Di Bono, M. G., & Zorzi, M. (2008). Decoding cognitive states from fMRI data using support vector regression. PsychNology Journal, 6(2), 189-201.
[13] Doppelmayr, M., & Weber, E. (2011). Effects of SMR and Theta/Beta neurofeedback on reaction times, spatial abilities, and creativity. Journal of Neurotherapy, 15(2), 115-129.
[14] Dubreuil-Vall L., Chau P., Ruffini G., Widge A. S., & Camprodon J. A. (2019). tDCS to the left DLPFC modulates cognitive and physiological correlates of executive function in a state-dependent manner. Brain Stimulation, 12(6), 1456-1463.
[15] Gaál, Z. A., & Czigler, I. (2018). Task-switching training and transfer: Age-related effects on late ERP components. Journal of Psychophysiology, 32(3), 106-130.
[16] Gal S., Tik N., Bernstein-Eliav M., & Tavor I. (2022). Predicting individual traits from unperformed tasks. NeuroImage, 249, Article 118920.
[17] Gerson A. D., Parra L. C., & Sajda P. (2006). Cortically coupled computer vision for rapid image search. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2), 174-179.
[18] Greene A. S., Gao S. Y., Scheinost D., & Constable R. T. (2018). Task-induced brain state manipulation improves prediction of individual traits. Nature Communications, 9(1), Article 2807.
[19] He T., Kong R., Holmes A. J., Nguyen M., Sabuncu M. R., Eickhoff S. B., & Yeo, B. T. T. (2020). Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. NeuroImage, 206, Article 116276.
[20] Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67.
[21] Hosseini S. M. H., Pritchard-Berman M., Sosa N., Ceja A., & Kesler S. R. (2016). Task-based neurofeedback training: A novel approach toward training executive functions. NeuroImage, 134, 153-159.
[22] Hu, J. F., & Min, J. L. (2018). Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model. Cognitive Neurodynamics, 12(4), 431-440.
[23] Huo L. J., Zheng Z. W., Li J., Wan W. Y., Cui X. Y., Chen S. Y., & Li J. (2018). Long-term transcranial direct current stimulation does not improve executive function in healthy older adults. Frontiers in Aging Neuroscience, 10, Article 298.
[24] Jiang R. T., Woo C. W., Qi S. L., Wu J., & Sui J. (2022). Interpreting brain biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging. IEEE Signal Processing Magazine, 39(4), 107-118.
[25] Jiang R. T., Zuo N. M., Ford J. M., Qi S. L., Zhi D. M., Zhuo C. J., & Sui J. (2020). Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships. NeuroImage, 207, Article 116370.
[26] Kadosh, K. C., & Staunton, G. (2019). A systematic review of the psychological factors that influence neurofeedback learning outcomes. NeuroImage, 185, 545-555.
[27] Kassai R., Futo J., Demetrovics Z., & Takacs Z. K. (2019). A meta-analysis of the experimental evidence on the near- and far-transfer effects among children' s executive function skills. Psychological Bulletin, 145(2), 165-188.
[28] Ke Y. F., Wang N. C., Du J. L., Kong L. H., Liu S., Xu M. P., & Ming D. (2019). The effects of transcranial direct current stimulation (tDCS) on working memory training in healthy young adults. Frontiers in Human Neuroscience, 13, Article 19.
[29] Khanam F., Hossain A. B. M. A., & Ahmad M. (2023). Electroencephalogram-based cognitive load level classification using wavelet decomposition and support vector machine. Brain-Computer Interfaces, 10(1), 1-15.
[30] Kim B., Erickson B. A., Fernandez-Nunez G., Rich R., Mentzelopoulos G., Vitale F., & Medaglia J. D. (2023). EEG phase can be predicted with similar accuracy across cognitive states after accounting for power and signal-to-noise ratio. eNeuro, 10(9), Article ENEURO.0050-23.2023.
[31] Leite J., Gonçalves Ó., Silva S., Branco D., Coelho P., & Carvalho S. (2020). Cross-hemispheric transcranial direct current stimulation over the parietal cortex impairs task switching performance. Psicologia, 43(1), 425-430.
[32] Li X. X., Niksirat K. S., Chen S. S., Weng D. D., Sarcar S., & Ren X. S. (2020). The impact of a multitasking-based virtual reality motion video game on the cognitive and physical abilities of older adults. Sustainability, 12(21), Article 9106.
[33] Li Y. M., Wang L., Jia M., Guo J. H., Wang H. J., & Wang M. W. (2017). The effects of high-frequency rTMS over the left DLPFC on cognitive control in young healthy participants. PLoS ONE, 12(6), Article e0179430.
[34] Linardatos P., Papastefanopoulos V., & Kotsiantis S. (2020). Explainable AI: A review of machine learning interpretability methods. Entropy, 23(1), Article 18.
[35] Llewellyn D. J., Lang I. A., Langa K. M., & Huppert F. A. (2008). Cognitive function and psychological well-being: Findings from a population-based cohort. Age and Ageing, 37(6), 685-689.
[36] López-de-Ipiña K., Martinez-de-Lizarduy U., Calvo P. M., Beitia B., García-Melero J., Fernández E., & Sanz P. (2020). On the analysis of speech and disfluencies for automatic detection of Mild Cognitive Impairment. Neural Computing and Applications, 32(20), 15761-15769.
[37] Loriette C., Amengual J. L., & Ben Hamed S. (2022). Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior. Frontiers in Neuroscience, 16, Article 811736.
[38] Loriette C., Ziane C., & Ben Hamed S. (2021). Neurofeedback for cognitive enhancement and intervention and brain plasticity. Revue Neurologique, 177(9), 1133-1144.
[39] Luna F. G., Román-Caballero R., Barttfeld P., Lupiáñez J., & Martín-Arévalo E. (2020). A high-definition tDCS and EEG study on attention and vigilance: Brain stimulation mitigates the executive but not the arousal vigilance decrement. Neuropsychologia, 142, Article 107447.
[40] Mack M., Stojan R., Bock O., & Voelcker-Rehage C. (2022). Cognitive-motor multitasking in older adults: A randomized controlled study on the effects of individual differences on training success. BMC Geriatrics, 22(1), Article 581.
[41] Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR), 9(1), 381-386.
[42] Mihalik A., Chapman J., Adams R. A., Winter N. R., Ferreira F. S., Shawe-Taylor J., & Alzheimer' s Disease Neuroimaging Initiative. (2022). Canonical correlation analysis and partial least squares for identifying brain-behavior associations: A tutorial and a comparative study. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 7(11), 1055-1067.
[43] Moffitt T. E., Arseneault L., Belsky D., Dickson N., Hancox R. J., Harrington H., & Caspi A. (2011). A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences of the United States of America, 108(7), 2693-2698.
[44] Muraskin J., Sherwin J., Lieberman G., Garcia J. O., Verstynen T., Vettel J. M., & Sajda P. (2017). Fusing multiple neuroimaging modalities to assess group differences in perception-action coupling. Proceedings of the IEEE, 105(1), 83-100.
[45] Nouchi R., Nouchi H., Dinet J., & Kawashima R. (2021). Cognitive training with neurofeedback using NIRS improved cognitive functions in young adults: Evidence from a randomized controlled trial. Brain Sciences, 12(1), Article 5.
[46] Pappa K., Biswas V., Flegal K. E., Evans J. J., & Baylan S. (2020). Working memory updating training promotes plasticity & behavioural gains: A systematic review & meta-analysis. Neuroscience and Biobehavioral Reviews, 118, 209-235.
[47] Pappa K., Flegal K. E., Baylan S., & Evans J. J. (2022). Working memory training: Taking a step back to retool and create a bridge between clinical and neuroimaging research methods. Applied Neuropsychology: Adult, 29(6), 1669-1680.
[48] Park S., Ryu S. H., Yoo Y., Yang J. J., Kwon H., Youn J. H., & Lee J. Y. (2018). Neural predictors of cognitive improvement by multi-strategic memory training based on metamemory in older adults with subjective memory complaints. Scientific Reports, 8(1), Article 1095.
[49] Pergher V., Au J., Alizadeh Shalchy M., Santarnecchi E., Seitz A., Jaeggi S. M., & Battelli L. (2022). The benefits of simultaneous tDCS and working memory training on transfer outcomes: A systematic review and meta-analysis. Brain Stimulation, 15(6), 1541-1551.
[50] Pervaiz U., Vidaurre D., Woolrich M. W., & Smith S. M. (2020). Optimising network modelling methods for fMRI. NeuroImage, 211, Article 116604.
[51] Pläschke R. N., Patil K. R., Cieslik E. C., Nostro A. D., Varikuti D. P., Plachti A., & Eickhoff S. B. (2020). Age differences in predicting working memory performance from network-based functional connectivity. Cortex, 132, 441-459.
[52] Priya B. L., Jayalakshmy S., K. Pragatheeswaran J. K., Saraswathi D., & Poonguzhali N. (2021). Scattering convolutional network based predictive model for cognitive activity of brain using empirical wavelet decomposition. Biomedical Signal Processing and Control, 66, Article 102501.
[53] Roc A., Pillette L., Mladenovic J., Benaroch C., N' Kaoua B., Jeunet C., & Lotte F. (2021). A review of user training methods in brain computer interfaces based on mental tasks. Journal of Neural Engineering, 18(1), Article 011002.
[54] Rosenberg M. D., Finn E. S., Scheinost D., Papademetris X., Shen X. L., Constable R. T., & Chun M. M. (2016). A neuromarker of sustained attention from whole-brain functional connectivity. Nature Neuroscience, 19(1), 165-171.
[55] Rosenberg M. D., Scheinost D., Greene A. S., Avery E. W., Kwon Y. H., Finn E. S., & Chun M. M. (2020). Functional connectivity predicts changes in attention observed across minutes, days, and months. Proceedings of the National Academy of Sciences of the United States of America, 117(7), 3797-3807.
[56] Sandeep S., Shelton C. R., Pahor A., Jaeggi S. M., & Seitz A. R. (2020). Application of machine learning models for tracking participant skills in cognitive training. Frontiers in Psychology, 11, Article 1532.
[57] Sdoia S., Zivi P., & Ferlazzo F. (2020). Anodal tDCS over the right parietal but not frontal cortex enhances the ability to overcome task set inhibition during task switching. PLoS ONE, 15(2), Article e0228541.
[58] Shani R., Tal S., Derakshan N., Cohen N., Enock P. M., McNally R., & Okon-Singer H. (2021). Personalized cognitive training: Protocol for individual-level meta-analysis implementing machine learning methods. Journal of Psychiatric Research, 138, 342-348.
[59] Shani R., Tal S., Zilcha-Mano S., & Okon-Singer H. (2019). Can machine learning approaches lead toward personalized cognitive training? Frontiers in Behavioral Neuroscience, 13, Article 64.
[60] Shibata K., Lisi G., Cortese A., Watanabe T., Sasaki Y., & Kawato M. (2019). Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback. NeuroImage, 188, 539-556.
[61] Shibata K., Watanabe T., Sasaki Y., & Kawato M. (2011). Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science, 334(6061), 1413-1415.
[62] Stock A. K., Popescu F., Neuhaus A. H., & Beste C. (2016). Single-subject prediction of response inhibition behavior by event-related potentials. Journal of Neurophysiology, 115(3), 1252-1262.
[63] Sudharsan, M., & Thailambal, G. (2023). Alzheimer' s disease prediction using machine learning techniques and principal component analysis (PCA). Materialstoday: Proceedings, 81, 182-190.
[64] Sui J., Jiang R. T., Bustillo J., & Calhoun V. (2020). Neuroimaging-based individualized prediction of cognition and behavior for mental disorders and health: Methods and promises. Biological Psychiatry, 88(11), 818-828.
[65] Taschereau-Dumouchel V., Cortese A., Lau H., & Kawato M. (2021). Conducting decoded neurofeedback studies. Social Cognitive and Affective Neuroscience, 16(8), 838-848.
[66] Taxali A., Angstadt M., Rutherford S., & Sripada C. (2021). Boost in test-retest reliability in resting state fMRI with predictive modeling. Cerebral Cortex, 31(6), 2822-2833.
[67] Tian, Y., & Zalesky, A. (2021). Machine learning prediction of cognition from functional connectivity: Are feature weights reliable? NeuroImage, 245, Article 118648.
[68] Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1), 267-288.
[69] Traut H. J., Guild R. M., & Munakata Y. (2021). Why does cognitive training yield inconsistent benefits? A meta-analysis of individual differences in baseline cognitive abilities and training outcomes. Frontiers in Psychology, 12, Article 662139.
[70] Turner B. M., Rodriguez C. A., Norcia T. M., McClure S. M., & Steyvers M. (2016). Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data. NeuroImage, 128, 96-115.
[71] Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A. N., & Polosukhin I. (2017). Attention is all you need. In Proceedings of the 31st international conference on neural information processing systems, Red Hook: Curran Associates Inc.
[72] Vieira B. H., Pamplona G. S. P., Fachinello K., Silva A. K., Foss M. P., & Salmon, C. E. G. (2022). On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting. Intelligence, 93, Article 101654.
[73] Vladisauskas M., Belloli L. M. L., Fernández Slezak D., & Goldin A. P. (2022). A machine learning approach to personalize computerized cognitive training interventions. Frontiers in Artificial Intelligence, 5, Article 788605.
[74] Wan W., Gao Z. L., Zhang Q. C., Gu Z. Z., Chang C., Peng C. K., & Cui X. R. (2023). Resting state EEG complexity as a predictor of cognitive performance. Physica A: Statistical Mechanics and Its Applications, 624, Article 128952.
[75] Wang Z. P., Zhou Y. J., Chen L., Gu B., Liu S., Xu M. P., & Ming D. (2019). A BCI based visual-haptic neurofeedback training improves cortical activations and classification performance during motor imagery. Journal of Neural Engineering, 16(6), Article 066012.
[76] Wu J. X., Li J. W., Eickhoff S. B., Scheinost D., & Genon S. (2023). The challenges and prospects of brain-based prediction of behaviour. Nature Human Behaviour, 7(8), 1255-1264.
[77] Xu Z. H., Bai Y. R., Zhao R., Hu H. M., Ni G. J., & Ming D. (2022). Decoding selective auditory attention with EEG using a transformer model. Methods, 204, 410-417.
[78] Xue Q. W., Wang X. Y., Li Y. H., & Guo W. W. (2023). Young novice drivers' cognitive distraction detection: Comparing support vector machines and random forest model of vehicle control behavior. Sensors, 23(3), Article 1345.
[79] Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100-1122.
[80] Yeung A. W. K., More S., Wu J. X., & Eickhoff S. B. (2022). Reporting details of neuroimaging studies on individual traits prediction: A literature survey. NeuroImage, 256, Article 119275.
[81] Yeung H. W., Stolicyn A., Buchanan C. R., Tucker-Drob E. M., Bastin M. E., Luz S., & Smith K. (2023). Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes. Human Brain Mapping, 44(5), 1913-1933.
[82] Yin Z. L., Wang Y., Dong M. H., Ren S. H., Hu H. H., Yin K. J., & Liang J. (2021). Special patterns of dynamic brain networks discriminate between face and non-face processing: A single-trial EEG study. Frontiers in Neuroscience, 15, Article 652920.
[83] Yoo K., Rosenberg M. D., Kwon Y. H., Lin Q., Avery E. W., Sheinost D., & Chun M. M. (2022). A brain-based general measure of attention. Nature Human Behaviour, 6(6), 782-795.
[84] Zang B. Y., Lin Y. F., Liu Z. W., & Gao X. R. (2021). A deep learning method for single-trial EEG classification in RSVP task based on spatiotemporal features of ERPs. Journal of Neural Engineering, 18(4), Article 0460c8.
[85] Zhai M. Y., Wang S. T., Wang Y. Z., & Wang D. J. (2022). An interpretable prediction method for university student academic crisis warning. Complex and Intelligent Systems, 8(1), 323-336.
[86] Zhang G. Y., Yao L., Zhang H., Long Z. Y., & Zhao X. J. (2013). Improved working memory performance through self-regulation of dorsal lateral prefrontal cortex activation using real-time fMRI. PLoS ONE, 8(8), Article e73735.
[87] Zhang, Y. Q., & Goh, W. B. (2021). Personalized task difficulty adaptation based on reinforcement learning. User Modeling and User-Adapted Interaction, 31(4), 753-784.
[88] Zhang Z., Liu Y., & Zhong S. H. (2023). GANSER: A self-supervised data augmentation framework for EEG-based emotion recognition. IEEE Transactions on Affective Computing, 14(3), 2048-2063.
[89] Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2), 301-320.