Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.

Lin H, Fang J, Zhang J, Zhang X, Piao W, Liu Y.

DOI: 10.3390/s24216815
Published: 2024-10-23
Container: Sensors (Basel)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/s24216815,
  title = {Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.},
  author = {Lin H and  Fang J and  Zhang J and  Zhang X and  Piao W and  Liu Y.},
  year = {2024},
  journal = {Sensors (Basel)},
  doi = {10.3390/s24216815},
  url = {https://doi.org/10.3390/s24216815}
}

RIS

TY  - JOUR
TI  - Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.
AU  - Lin H
AU  -  Fang J
AU  -  Zhang J
AU  -  Zhang X
AU  -  Piao W
AU  -  Liu Y.
PY  - 2024
JO  - Sensors (Basel)
DO  - 10.3390/s24216815
UR  - https://doi.org/10.3390/s24216815
ER  -

APA

H, L., J, F., J, Z., X, Z., W, P., & Y., L. (2024). Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.. Sensors (Basel). https://doi.org/10.3390/s24216815

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europe-pmc · retrieved 2026-09-27T03:40:50.498Z