{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T05:20:47Z","timestamp":1787721647055,"version":"build-2784847793"},"reference-count":39,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangxi Natural Science Foundation","award":["2025GXNSFAA069804"],"award-info":[{"award-number":["2025GXNSFAA069804"]}]},{"name":"Capacity Building of Guangxi Key Laboratory of Automatic Detecting Technology and Instruments","award":["GuiKeLT2600640021"],"award-info":[{"award-number":["GuiKeLT2600640021"]}]},{"award":["GuiKeLT2600640021"],"award-info":[{"award-number":["GuiKeLT2600640021"]}],"id":[{"id":"https:\/\/ror.org\/03wpem007","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sci.","Brain Sciences"],"abstract":"<jats:p>Background: Major depressive disorder (MDD) is a common psychiatric disorder. Electroencephalography (EEG) provides physiological information for depression detection, but many existing methods rely on dense multichannel recordings, limiting their use in lightweight EEG screening. Frontal sparse-channel EEG reduces acquisition burden but provides limited spatial information, requiring effective within-window representation learning and cross-window temporal modelling. Methods: We propose FSC-BiMamba, a dual-branch bidirectional Mamba network for subject-independent window-level MDD classification from frontal sparse-channel EEG. For each window, a Time\u2013Frequency Map Encoder (TFME) learns local time\u2013frequency patterns, while a Frequency-Domain Statistical Descriptor Encoder (FSDE) encodes complementary frequency-domain statistical descriptors. An Adaptive Dual-Token Fusion (ADTF) module integrates the resulting tokens through feature-wise gating to form a unified window representation. Representations from eight consecutive windows are then processed by a bidirectional Mamba (BiMamba) module to capture cross-window context. Finally, a softmax classifier converts each contextualized representation into a window-level class prediction. Results: Performance was evaluated using stratified subject-independent five-fold cross-validation. FSC-BiMamba achieved window-level accuracies of 82.70 \u00b1 2.37% and 84.55 \u00b1 8.01% on MODMA and Mumtaz2016, respectively. Together with its compact architecture, these results indicate a favourable balance between classification performance and model size. Conclusions: FSC-BiMamba effectively integrates complementary window-level representations with cross-window temporal context. It achieved the highest accuracy among the compared models on both datasets, demonstrating a favourable performance\u2013size trade-off for lightweight EEG-based MDD screening.<\/jats:p>","DOI":"10.3390\/brainsci16090902","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T14:49:34Z","timestamp":1787582974000},"page":"902","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FSC-BiMamba: A Dual-Branch Bidirectional Mamba Network for Frontal Sparse-Channel EEG Depression Detection"],"prefix":"10.3390","volume":"16","author":[{"given":"Yaqiang","family":"Che","sequence":"first","affiliation":[{"name":"Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5568-767X","authenticated-orcid":false,"given":"Chunting","family":"Wan","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhao","family":"Yang","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8112-183X","authenticated-orcid":false,"given":"Dongyi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"GBD 2019 Mental Disorders Collaborators (2022). Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990\u20132019. Lancet Psychiatry, 9, 137\u2013150.","DOI":"10.1016\/S2215-0366(21)00395-3"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.nicl.2017.07.006","article-title":"Frontal alpha asymmetry as a diagnostic marker in depression: Fact or fiction?","volume":"16","author":"Vollebregt","year":"2017","journal-title":"NeuroImage Clin."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Elnaggar, K., El-Gayar, M.M., and Elmogy, M. (2025). Depression detection and diagnosis based on electroencephalogram (EEG) analysis: A comprehensive review. Diagnostics, 15.","DOI":"10.3390\/diagnostics15020210"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6277690","DOI":"10.1155\/jece\/6277690","article-title":"Survey of deep learning and machine learning approaches for major depressive disorder detection using EEG data","volume":"2025","author":"Balakrishnan","year":"2025","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106741","DOI":"10.1016\/j.compbiomed.2023.106741","article-title":"Machine learning-based approaches for detecting depression using audiovisual and EEG modalities: A comprehensive review","volume":"159","author":"Yasin","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mohammadi, M., Al-Azab, F., Raahemi, B., Richards, G., Jaworska, N., Smith, D., de la Salle, S., Blier, P., and Knott, V. (2015). Data mining EEG signals in depression for their diagnostic value. BMC Med. Inform. Decis. Mak., 15.","DOI":"10.1186\/s12911-015-0227-6"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liao, S.-C., Wu, C.-T., Huang, H.-C., Cheng, W.-T., and Liu, Y.-H. (2017). Major depression detection from EEG signals using kernel eigen-filter-bank common spatial patterns. Sensors, 17.","DOI":"10.3390\/s17061385"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.cmpb.2012.10.008","article-title":"Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal","volume":"109","author":"Hosseinifard","year":"2013","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5391","DOI":"10.1002\/hbm.23730","article-title":"Deep learning with convolutional neural networks for EEG decoding and visualization","volume":"38","author":"Schirrmeister","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","article-title":"EEGNet: A compact convolutional neural network for EEG-based brain-computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"73804","DOI":"10.1109\/ACCESS.2022.3190502","article-title":"Automated detection of major depressive disorder with EEG signals: A time series classification using deep learning","volume":"10","author":"Rafiei","year":"2022","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1109\/TNSRE.2022.3230250","article-title":"EEG Conformer: Convolutional Transformer for EEG decoding and visualization","volume":"31","author":"Song","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"173109","DOI":"10.1109\/ACCESS.2024.3493140","article-title":"SLiTRANet: An EEG-based automated diagnosis framework for major depressive disorder monitoring using a novel LGCN and Transformer-based hybrid deep learning approach","volume":"12","author":"Singh","year":"2024","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Umair, M., Ahmad, J., Alasbali, N., Saidani, O., Hanif, M., Khattak, A.A., and Khan, M.S. (2025). Decentralized EEG-based detection of major depressive disorder via transformer architectures and split learning. Front. Comput. Neurosci., 19.","DOI":"10.3389\/fncom.2025.1569828"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/TNSRE.2020.3043426","article-title":"Brain functional networks based on resting-state EEG data for major depressive disorder analysis and classification","volume":"29","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/TNSRE.2021.3059429","article-title":"Enhancing EEG-based classification of depression patients using spatial information","volume":"29","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"106051","DOI":"10.1016\/j.bspc.2024.106051","article-title":"MGSN: Depression EEG lightweight detection based on multiscale DGCN and SNN for multichannel topology","volume":"92","author":"Chen","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1592","DOI":"10.1109\/TAFFC.2025.3527459","article-title":"TFAGL: A novel agent graph learning method using time-frequency EEG for major depressive disorder detection","volume":"16","author":"Xu","year":"2025","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jia, J., Zhang, B., Lv, H., Xu, Z., Hu, S., and Li, H. (2022). CR-GCN: Channel-relationships-based graph convolutional network for EEG emotion recognition. Brain Sci., 12.","DOI":"10.3390\/brainsci12080987"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/TCSS.2025.3576373","article-title":"ACM-GNN: Adaptive cluster-oriented modularity graph neural network for EEG depression detection","volume":"13","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, C.-T., Huang, H.-C., Huang, S., Chen, I.-M., Liao, S.-C., Chen, C.-K., Lin, C., Lee, S.-H., Chen, M.-H., and Tsai, C.-F. (2021). Resting-state EEG signal for major depressive disorder detection: A systematic validation on a large and diverse dataset. Biosensors, 11.","DOI":"10.3390\/bios11120499"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lei, X., Ji, W., Guo, J., Wu, X., Wang, H., Zhu, L., and Chen, L. (2022). Research on the method of depression detection by single-channel EEG sensor. Front. Psychol., 13.","DOI":"10.3389\/fpsyg.2022.850159"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"594","DOI":"10.55730\/1300-0632.4146","article-title":"Automatic detection of depression using a CNN-Transformer model based on low-channel EEG data","volume":"33","author":"Yu","year":"2025","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.pscychresns.2014.02.010","article-title":"Functional connectivity in major depression: Increased phase synchronization between frontal cortical EEG-source estimates","volume":"222","author":"Olbrich","year":"2014","journal-title":"Psychiatry Res. NeuroImaging"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"18008","DOI":"10.1038\/s41598-025-02452-7","article-title":"Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform","volume":"15","author":"Bagherzadeh","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1002\/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C","article-title":"Measuring phase synchrony in brain signals","volume":"8","author":"Lachaux","year":"1999","journal-title":"Hum. Brain Mapp."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1016\/j.neuroimage.2011.01.055","article-title":"An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias","volume":"55","author":"Vinck","year":"2011","journal-title":"NeuroImage"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.neucom.2012.12.039","article-title":"Classification of covariance matrices using a Riemannian-based kernel for BCI applications","volume":"112","author":"Barachant","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18\u201323 June 2018, IEEE.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_30","unstructured":"Gu, A., and Dao, T. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv."},{"key":"ref_31","unstructured":"Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., and Wang, X. (2024). Vision Mamba: Efficient visual representation learning with bidirectional state space model. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"38762","DOI":"10.1038\/s41598-025-22684-x","article-title":"Adaptive long-range modeling of EEG and ECG with Mamba and dynamic graph learning","volume":"15","author":"Hu","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e144","DOI":"10.1017\/S003329172610436X","article-title":"Multimodal fusion of brain imaging and proteomics reveals a brain\u2013body pathway linking depression and metabolic dysfunction","volume":"56","author":"Lian","year":"2026","journal-title":"Psychol. Med."},{"key":"ref_34","unstructured":"Cai, H., Gao, Y., Sun, S., Li, N., Tian, F., Xiao, H., Li, J., Yang, Z., Li, X., and Zhao, Q. (2020). MODMA dataset: A multi-modal open dataset for mental-disorder analysis. arXiv."},{"key":"ref_35","unstructured":"Mumtaz, W. (2026, July 10). MDD Patients and Healthy Controls EEG Data (New). Figshare. Dataset. Available online: https:\/\/figshare.com\/articles\/dataset\/EEG_Data_New\/4244171\/2."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2505413","DOI":"10.1109\/TIM.2021.3053999","article-title":"DeprNet: A deep convolution neural network framework for detecting depression using EEG","volume":"70","author":"Seal","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, Z., Ma, Z., Liu, W., An, Z., and Huang, F. (2022). A depression diagnosis method based on the hybrid neural network and attention mechanism. Brain Sci., 12.","DOI":"10.3390\/brainsci12070834"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ji, Z., Liu, K., Ma, W., Ning, X., and Gao, Y. (2026). Subject-independent depression recognition from EEG using an improved bidirectional LSTM with dynamic vector routing. Bioengineering, 13.","DOI":"10.3390\/bioengineering13030358"},{"key":"ref_39","unstructured":"Zhang, Y., Zhu, K., Gao, H., Wang, D., Guo, C., Shen, J., and Hu, B. (2027). A Novel Personalized Wearable Healthcare Framework: Exploring EEG Patterns for Depression Monitoring. Front. Comput. Sci., 21, Available online: https:\/\/journal.hep.com.cn\/fcs\/EN\/10.1007\/s11704-025-50997-7."}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/16\/9\/902\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T04:27:49Z","timestamp":1787718469000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/16\/9\/902"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,24]]},"references-count":39,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["brainsci16090902"],"URL":"https:\/\/doi.org\/10.3390\/brainsci16090902","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,24]]}}}