Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. arXiv. https://doi.org/10.48550/arXiv.1907.10902

Article  Google Scholar 

Alexander, L. M., Escalera, J., Ai, L., Andreotti, C., Febre, K., Mangone, A., Vega-Potler, N., Langer, N., Alexander, A., Kovacs, M., Litke, S., O’Hagan, B., Andersen, J., Bronstein, B., Bui, A., Bushey, M., Butler, H., Castagna, V., Camacho, N., & Milham, M. P. (2017). An open resource for transdiagnostic research in pediatric mental health and learning disorders. Scientific Data, 4(1), Article 170181. https://doi.org/10.1038/sdata.2017.181

Article  PubMed  PubMed Central  Google Scholar 

Ashwood, K. L., Gillan, N., Horder, J., Hayward, H., Woodhouse, E., McEwen, F. S., Findon, J., Eklund, H., Spain, D., Wilson, C. E., Cadman, T., Young, S., Stoencheva, V., Murphy, C. M., Robertson, D., Charman, T., Bolton, P., Glaser, K., Asherson, P., & Murphy, D. G. (2016). Predicting the diagnosis of autism in adults using the Autism-Spectrum quotient (AQ) questionnaire. Psychological Medicine, 46(12), 2595–2604. https://doi.org/10.1017/S0033291716001082

Article  PubMed  PubMed Central  CAS  Google Scholar 

Ayrolles, A., Brun, F., Chen, P., Djalovski, A., Beauxis, Y., Delorme, R., Bourgeron, T., Dikker, S., & Dumas, G. (2021). HyPyP: A hyperscanning python pipeline for inter-brain connectivity analysis. Social Cognitive and Affective Neuroscience, 16(1–2), 72–83. https://doi.org/10.1093/scan/nsaa141

Article  PubMed  Google Scholar 

Banville, H., Chehab, O., Hyvärinen, A., Engemann, D.-A., & Gramfort, A. (2021). Uncovering the structure of clinical EEG signals with self-supervised learning. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/abca18

Article  PubMed  Google Scholar 

Bolis, D., Dumas, G., & Schilbach, L. (2022). Interpersonal attunement in social interactions: From collective psychophysiology to inter-personalized psychiatry and beyond. Philosophical Transactions of the Royal Society B: Biological Sciences, 378(1870), Article 20210365. https://doi.org/10.1098/rstb.2021.0365

Article  Google Scholar 

Bottema-Beutel, K., Kim, S. Y., & Crowley, S. (2019). A systematic review and meta-regression analysis of social functioning correlates in autism and typical development. Autism Research, 12(2), 152–175. https://doi.org/10.1002/aur.2055

Article  PubMed  Google Scholar 

Bouallegue, G., Djemal, R., Alshebeili, S. A., & Aldhalaan, H. (2020). A dynamic filtering DF-RNN deep-learning-based approach for EEG-based neurological disorders diagnosis. IEEE Access, 8, 206992–207007. https://doi.org/10.1109/ACCESS.2020.3037995

Article  Google Scholar 

Bishop D. V. (2013). Cerebral asymmetry and language development: cause, correlate, or consequence?. Science, 340(6138), 1230531. https://doi.org/10.1126/science.1230531

Brihadiswaran, G., Haputhanthri, D., Gunathilaka, S., Meedeniya, D., & Jayarathna, S. (2019). EEG-based processing and classification methodologies for autism spectrum disorder: A review. Journal of Computer Science, 15(8), 1161–1183. https://doi.org/10.3844/jcssp.2019.1161.1183

Article  Google Scholar 

Brookshire, G., Kasper, J., Blauch, N. M., Wu, Y. C., Glatt, R., Merrill, D. A., Gerrol, S., Yoder, K. J., Quirk, C., & Lucero, C. (2024). Data leakage in deep learning studies of translational EEG. Frontiers in Neuroscience, 18, 1373515. https://doi.org/10.3389/fnins.2024.1373515

Article  PubMed  PubMed Central  Google Scholar 

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (Vol. 119, pp. 1597–1607). https://arxiv.org/abs/2002.05709

Cohen, M. X. (2017). Where does EEG come from and what does it mean? Trends in Neurosciences, 40(4), 208–218. https://doi.org/10.1016/j.tins.2017.02.004

Article  PubMed  CAS  Google Scholar 

Conner, C. M., Cramer, R. D., & McGonigle, J. J. (2019). Examining the diagnostic validity of autism measures among adults in an outpatient clinic sample. Autism in Adulthood, 1(1), 60–68. https://doi.org/10.1089/aut.2018.0023

Article  PubMed  PubMed Central  Google Scholar 

Crane, L., Batty, R., Adeyinka, H., Goddard, L., Henry, L. A., & Hill, E. L. (2018). Autism diagnosis in the United Kingdom: Perspectives of autistic adults, parents and professionals. Journal of Autism and Developmental Disorders, 48(11), 3761–3772. https://doi.org/10.1007/s10803-018-3639-1

Article  PubMed  PubMed Central  Google Scholar 

Cui, X., Bryant, D. M., & Reiss, A. L. (2012). NIRS-based hyperscanning reveals increased interpersonal coherence in superior frontal cortex during cooperation. NeuroImage, 59(3), 2430–2437. https://doi.org/10.1016/j.neuroimage.2011.09.003

Article  PubMed  Google Scholar 

Dawson, G., Finley, C., Phillips, S., & Lewy, A. (1989). A comparison of hemispheric asymmetries in speech-related brain potentials of autistic and dysphasic children. Brain and Language, 37(1), 26–41. https://doi.org/10.1016/0093-934x(89)90099-0

Article  PubMed  CAS  Google Scholar 

Del Pup, F., Zanola, A., Tshimanga, L. F., Bertoldo, A., Finos, L., & Atzori, M. (2025). The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: A preliminary study. Computers in Biology and Medicine, 196, 110608. https://doi.org/10.1016/j.compbiomed.2025.110608

Article  PubMed  Google Scholar 

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171–4186). https://aclanthology.org/N19-1423/

Dong, H., Chen, D., Zhang, L., Hengjin, ke, & Li, X. (2021). Subject Sensitive EEG Discrimination with Fast Reconstructable CNN Driven by Reinforcement Learning: A Case Study of ASD Evaluation. Neurocomputing, 449. https://doi.org/10.1016/j.neucom.2021.04.009

Dumas, G. (2022). From inter-brain connectivity to inter-personal psychiatry. World Psychiatry, 21(2), 214–215. https://doi.org/10.1002/wps.20987

Article  PubMed  PubMed Central  Google Scholar 

Dumas, G., Nadel, J., Soussignan, R., Martinerie, J., & Garnero, L. (2010). Inter-brain synchronization during social interaction. PLoS One, 5(8), e12166. https://doi.org/10.1371/journal.pone.0012166

Article  PubMed  PubMed Central  CAS  Google Scholar 

Dumas, G., Soussignan, R., Hugueville, L., Martinerie, J., & Nadel, J. (2014). Revisiting mu suppression in autism spectrum disorder. Brain Research, 1585, 108–119. https://doi.org/10.1016/j.brainres.2014.08.035

Article  PubMed  CAS  Google Scholar 

Dvorak, D., Shang, A., Abdel-Baki, S., Suzuki, W., & Fenton, A. A. (2018). Cognitive behavior classification from scalp EEG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(4), 729–739. https://doi.org/10.1109/TNSRE.2018.2797547

Article  PubMed  PubMed Central  Google Scholar 

Ecker, C., Bookheimer, S. Y., & Murphy, D. G. M. (2015). Neuroimaging in autism spectrum disorder: Brain structure and function across the lifespan. The Lancet Neurology, 14(11), 1121–1134. https://doi.org/10.1016/S1474-4422(15)00050-2

Article  PubMed  Google Scholar 

Engemann, D. A., Raimondo, F., King, J. R., Rohaut, B., Louppe, G., Faugeras, F., Annen, J., Cassol, H., Gosseries, O., Fernandez-Slezak, D., Laureys, S., Naccache, L., Dehaene, S., & Sitt, J. D. (2018). Robust EEG-based cross-site and cross-protocol classification of States of consciousness. Brain, 141(11), 3179–3192. https://doi.org/10.1093/brain/awy251

Article  PubMed  Google Scholar 

Fitzgerald, P. J., & Watson, B. O. (2018). Gamma oscillations as a biomarker for major depression: An emerging topic. Translational Psychiatry, 8(1), 1–11. https://www.nature.com/articles/s41398-018-0239-y

Article  CAS  Google Scholar 

Fraschini, M., Demuru, M., Crobe, A., Marrosu, F., Stam, C. J., & Hillebrand, A. (2016). The effect of epoch length on estimated EEG functional connectivity and brain network organisation. Journal of Neural Engineering, 13(3), 036015. https://doi.org/10.1088/1741-2560/13/3/036015

Article  PubMed  Google Scholar 

Gemein, L. A. W., Schirrmeister, R. T., Chrabąszcz, P., Wilson, D., Boedecker, J., Schulze-Bonhage, A., Hutter, F., & Ball, T. (2020). Machine-learning-based diagnostics of EEG pathology. Neuroimage, 220, 117021. https://doi.org/10.1016/j.neuroimage.2020.117021

Article  PubMed  Google Scholar 

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. https://www.deeplearningbook.org/

Gurau, O., Bosl, W. J., & Newton, C. R. (2017). How useful is electroencephalography in the diagnosis of autism spectrum disorders and the delineation of subtypes: A systematic review. Frontiers in Psychiatry, 8, 121. https://doi.org/10.3389/fpsyt.2017.00121

Article  PubMed  PubMed Central  Google Scholar 

He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9729–9738). https://arxiv.org/abs/1911.05722

Hirsch, J., Adam Noah, J., Zhang, X., Dravida, S., & Ono, Y. (2018). A cross-brain neural mechanism for human-to-human verbal communication. Social Cognitive and Affective Neuroscience, 13(9), 907–920. https://doi.org/10.1093/scan/nsy070

Article  PubMed  PubMed Central  Google Scho