Brain-computer interface-based on deep learning in preventing depression

Lun Xi

doi:10.54254/2753-8818/23/20231007

Article Information

Received: 29 November 2023

Published: 20 December 2023

DOI: https://doi.org/10.54254/2753-8818/23/20231007

Article Type: Research Article

Cite this article

Xi,L. (2023). Brain-computer interface-based on deep learning in preventing depression. Theoretical and Natural Science,23,1-6.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About this Volume

Volume Title: TNS Vol.23

Part of Series: Theoretical and Natural Science

ISSN: 2753-8818 (Print) / 2753-8826 (Online)

Copyright & License

[1]Adolphs, R. (2017). How should neuroscience study emotions? By distinguishing emotion states, concepts and experiences. Soc. Cogn. Affect. Neurosci. 12(1), 24–31.

[2]Arac, A., Zhao, P., Dobkin, B. H., Carmichael, S. T., and Golshani, P. (2019). DeepBehavior: a deep learning toolbox for automated analysis of animal and human behavior imaging data. Front. Syst. Neurosci. 13, 20.

[3]Aragona, B., and Wang, Z. (2009). Dopamine regulation of social choice in a monogamous rodent species. Front. Behav. Neurosci. 3, 15.

[4]Arel, I., Rose, D. C., and Karnowski, T. P. (2010). Deep machine learning - a new frontier in artificial intelligence research [Research Frontier]. IEEE Comput. Int. Mag. 5, 13–18.

[5]Batty, E., Whiteway, M., Saxena, S., Biderman, D., Abe, T., Musall, S., et al. (2019). BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. In Advances in Neural Information Processing Systems, eds Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d’Alché-Buc, and Emily B. Fox (Red Hook, NY, USA: Curran Associates, Inc.), 15706–15717.

[6]Bohic, M., Pattison, L. A., Jhumka, Z. A., Rossi, H., Thackray, J. K., Ricci, M., et al. (2021). Mapping the signatures of inflammatory pain and its relief. bioRxiv [Preprint].

[7]Bryan, J. D., and Levinson, S. E. (2015). Autoregressive hidden Markov model and the speech signal. Procedia Comput. Sci. 61, 328–333.

[8]Buhle, J. T., Kober, H., Ochsner, K. N., Mende-Siedlecki, P., Weber, J., Hughes, B. L., et al. (2013). Common representation of pain and negative emotion in the midbrain periaqueductal gray. Soc. Cogn. Affect. Neurosci. 8(6), 609–616.

[9]Campos-Ordoñez, T., Alcalá, E., Ibarra-Castañeda, N., Buriticá, J., and González-Pérez, Ó. (2022). Chronic exposure to cyclohexane induces stereotypic circling, hyperlocomotion and anxiety-like behavior associated with atypical c-Fos expression in motor- and anxiety-related brain regions. Behav. Brain Res. 418, 113664.

[10]Chang, B., Choi, Y., Jeon, M., et al. (2019). ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder. Genes (Basel), 10, 907.

[11]Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., et al. (2016). Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 3(3), 243–250.

[12]Dvornek, N. C., Ventola, P., Duncan, J. S. (2018). COMBINING PHENOTYPIC AND RESTING-STATE FMRI DATA FOR AUTISM CLASSIFICATION WITH RECURRENT NEURAL NETWORKS. Proc IEEE Int Symp Biomed Imaging 725–728.

[13]Erguzel, T. T., Ozekes, S., Gultekin, S., Tarhan, N., Hizli Sayar, G., Bayram, A. (2015). Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance. Psychiatry Investig 12(1), 61–65.

[14]Ermers, N. J., Hagoort, K., Scheepers, F. E. (2020). The Predictive Validity of Machine Learning Models in the Classification and Treatment of Major Depressive Disorder: State of the Art and Future Directions. Front Psychiatry, 11, 472.

[15]Farruque, N., Zaiane, O., Goebel, R. (2020). Augmenting Semantic Representation of Depressive Language: From Forums to Microblogs. In Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (Eds.), Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science, vol 11908. Springer, Cham.

[16]Fong, R., Patrick, M., Vedaldi, A. (2019). Understanding deep networks via extremal perturbations and smooth masks. In Proceedings of the IEEE International Conference on Computer Vision, pp. 2950–2958.

[17]Fong, R. C., Vedaldi, A. (2017). Interpretable explanations of black boxes by meaningful perturbation. In Proceedings of the IEEE International Conference on Computer Vision, pp. 3429–3437.