{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T05:21:25Z","timestamp":1788326485083,"version":"build-2803163510"},"reference-count":45,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ningbo Natural Science Foundation project","award":["2024J215"],"award-info":[{"award-number":["2024J215"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY23F020013"],"award-info":[{"award-number":["LY23F020013"]}]},{"award":["LY23F020013"],"award-info":[{"award-number":["LY23F020013"]}],"id":[{"id":"https:\/\/ror.org\/04aqat463","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61972350"],"award-info":[{"award-number":["61972350"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["61972350"],"award-info":[{"award-number":["61972350"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Ningbo 2025 Major Project of Science and Technology Innovation","award":["2021Z109"],"award-info":[{"award-number":["2021Z109"]}]},{"name":"Major Technological Innovation Projects of Ningbo High tech Zone","award":["2022BCX050002"],"award-info":[{"award-number":["2022BCX050002"]}]},{"name":"2023 Ningbo Key Research Plan","award":["2023Z040"],"award-info":[{"award-number":["2023Z040"]}]},{"name":"Ningbo Science and Technology Innovation Yongjiang 2035 Key Technology Breakthrough Plan","award":["2024Z261"],"award-info":[{"award-number":["2024Z261"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Wearable brain-imaging devices have been developed to meet the growing demand in the healthcare industry for long-term monitoring of brain signals in natural conditions, such as monitoring brain diseases and emotions. However, conventional EEG and fNIRS (functional near-infrared spectroscopy) devices are often expensive, bulky and difficult to operate, making it difficult to monitor patients for long periods in natural conditions. To address these issues, this article proposes a low-cost, portable and multimodal wearable brain signal acquisition scheme. It combines EEG (electroencephalography) and fNIRS to reflect brain activity from different perspectives. In order to make it more wearable, a conductive rubber material is used as the electrode for the EEG. In this study, the corresponding experiments were used to verify the performance of the device. The first is the measurement of internal system noise, which satisfies the data acquisition of EEG and fNIRS at different gain levels. The \u03b1-rhythm experiment and the SSVEP (steady-state visual evoked potentials) experiment were used to validate the performance of EEG data acquisition. The performance of the fNIRS was verified by measuring changes in cerebral blood oxygen during breath-hold and breathing. In addition, by decomposing the raw fNIRS data with the VMD (variational mode decomposition) algorithm and performing correlation analysis, heart rate information was separated from the data. The performance of the proposed device was validated in the above experiments, confirming the feasibility of the design for multimodal data acquisition and meeting the requirements for portability and wearability. Furthermore, the proposed device was tested with 31 subjects (15 depressive subjects) to detect depression. Experiments proved the effectiveness of the multimodal signals, which outperformed single modal and surpassed EEG by 8.4% and fNIRS by 23.5%.<\/jats:p>","DOI":"10.3390\/bios16090478","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T09:21:19Z","timestamp":1788168079000},"page":"478","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Low-Cost Wearable Multimodal Brain Signal Acquisition System Integrating EEG and fNIRS for Depression Detection"],"prefix":"10.3390","volume":"16","author":[{"given":"Zihan","family":"Fei","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, NingboTech University, Ningbo 315100, 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