The application of integrating electroencephalograph-based emotion recognition technology into brain–computer interface systems for the treatment of depression: a narrative reviewZhang, Min1; Yang, Yi2,3,4,5,6; Zhao, Yongmei7; Sui, Changbai8; Sui, Ying9; Jiang, Youzhi10; Liu, Kanlai11; Yang, Shuai12; Wang, Liqin13; Chen, Bingjie14; Zhang, Rui15; Zhang, Qun16; Huang, Zhisheng17,18,*; Huang, Manli19,20,21,22,* 1 Zibo Hongsheng Medical Technology Co., Ltd., Zibo, Shandong Province, China 2 Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China 3 China National Clinical Research Center for Neurological Diseases, Beijing, China 4 Innovative Center, Beijing Institute of Brain Disorders, Beijing, China 5 Department of Neurosurgery, Chinese Institute for Brain Research, Beijing, China 6 Medical Research Council Brain Network Dyamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK 7 Department of Emergency, the Kailuan General Hospital, Tangshan, Hebei Province, China 8 Department of Neurology, Yantaishan Hospital, Yantai, Shandong Province, China 9 Institute of Tissue Regeneration and Wound Repair, Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, China 10 Department of Internal Medicine, PKU Care Luzhong Hospital, Zibo, Shandong Province, China 11 Department of general surgery, Jimo District People’s Hospital of Qingdao, Qingdao, Shandong Province, China 12 Department of Geriatrics, PKU Care Luzhong Hospital, Zibo, Shandong Province, China 13 Department of Medical Affairs Department, PKU Care Luzhong Hospital, Zibo, Shandong Province, China 14 Department of Radiology, PKU Care Luzhong Hospital, Zibo, Shandong Province, China 15 Department of Special Inspection, Gaomi People’s Hospital, Weifang, Shandong Province, China 16 Department of Gastroenterology, Gaomi People’s Hospital, Weifang, Shandong Province, China 17 Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China 18 Department of AI, VU University Amsterdam, Amsterdam, Netherlands 19 Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China 20 Zhejiang Key Laboratory of Precision Psychiatry, Hangzhou, Zhejiang Province, China 21 Brain Research Institute of Zhejiang University, Hangzhou, Zhejiang Province, China 22 Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, Zhejiang Province, China *Correspondence to: Manli Huang, PhD, huangmanli@zju.edu.cn; Zhisheng Huang, PhD, z.huang@vu.nl or huang.zhisheng.nl@gmail.com. Funding: This work was supported by the National Natural Science Foundation of China, No. 82271562 (to MH); Science and Technology Innovation 2030, No. 2022ZD0205300; the Key Research and Development Program of Zhejiang Province of China, No. 2023C03077 (to MH); Chinese Institute for Brain Research Youth Scholar Program No. 2022-NKX-XM-02; National Natural Science Foundation of China No. 82371197; and Natural Science Foundation of Beijing Municipality, No. 7232049. This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License (http://creativecommons.org/licenses/by-nc-sa/4.0/), which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. Advanced Technology in Neuroscience 1(2):p 188-200, December 2024. | DOI: 10.4103/ATN.ATN-D-24-00018 Dysregulation of the prefrontal cortex, amygdala, and hippocampus, along with alterations in P300 amplitude and abnormalities in the theta and beta bands, has been closely linked to the onset and pathophysiology of depression. Consequently, integrating electroencephalograph-based emotion recognition technology into brain‒computer interface systems offers the potential for real-time identification and modulation of emotional states through continuous interaction between the brain‒computer interface system and brain activity. This closed-loop system could precisely control neural stimulation in brain regions associated with emotional disorders, potentially alleviating the distressing memories of traumatic events. Although the efficacy of the brain‒computer interface in treating depression still requires validation through extensive clinical trials, its inherent real-time feedback and adaptive capabilities present a promising avenue for depression therapy. This review aims to explore the neuroanatomical mechanisms and neural activity patterns associated with depression and evaluate the potential of brain‒computer interface technology as a treatment modality. The objectives include summarizing key brain regions and neural networks involved in depression, analyzing their activity patterns, and assessing the impact of brain‒computer interface technology on these regions to provide theoretical support for future clinical trials. Significant functional abnormalities have been identified in the prefrontal cortex, amygdala, and hippocampus of patients with depression. The gray matter density, functional connectivity, and neural activity in these regions are closely associated with the severity of depressive symptoms. Common features in patients with depression include a reduced P300 amplitude and increased θ and α current density. Brain‒computer interface technology has demonstrated potential in modulating these abnormal neural activities, particularly in emotion recognition and regulation. When combined with techniques such as repetitive transcranial magnetic stimulation and deep brain stimulation, brain‒computer interface may provide effective interventions for managing emotional states in patients with depression. This review confirms the association between depression and functional abnormalities in specific brain regions and suggests that brain‒computer interface technology offers promising therapeutic potential by modulating abnormal neural activity. Brain‒computer interface could represent a novel treatment approach for depression. Future research should focus on validating the practical applications, efficacy, and safety of brain‒computer interface in treating depression. 摘要 前额叶皮层、杏仁核和海马的失调，以及P300 振幅的改变、θ 和 beta 波段的异常，与抑郁症的发病和病理生理学密切相关。因此，将基于脑电图的情绪识别技术整合到脑机接口技术系统中，通过脑机接口技术系统与大脑活动之间的持续互动，为实时识别和调节情绪状态提供了可能。这种闭环系统可以精确控制对与情绪失调有关的脑区的神经刺激，从而有可能减轻创伤事件的痛苦记忆。尽管脑机接口治疗抑郁症的疗效仍需要通过大量临床试验来验证，但其固有的实时反馈和自适应能力为抑郁症治疗提供了一条前景广阔的途径。此综述旨在探讨与抑郁症相关的神经解剖机制和神经活动模式，并评估脑机接口技术作为一种治疗方式的潜力。其目的包括总结与抑郁症有关的关键脑区和神经网络，分析其活动模式，并评估 脑机接口技术对这些脑区的影响，从而为未来的临床试验提供理论支持。研究发现，抑郁症患者的前额叶皮层、杏仁核和海马存在明显的功能异常。这些区域的灰质密度、功能连接和神经活动与抑郁症状的严重程度密切相关。抑郁症患者的共同特征包括P300振幅降低、θ和α电流密度增加。脑机接口技术在调节这些异常神经活动，尤其是情绪识别和调节方面具有潜力。当与重复经颅磁刺激和深部脑刺激等技术相结合时，脑机接口技术可为抑郁症患者的情绪管理提供有效的干预。此综述总结了抑郁症与特定脑区功能异常之间的联系，并表明脑机接口技术可通过调节异常神经活动提供良好的治疗潜力。脑机接口技术可能是治疗抑郁症的一种新方法。未来的研究应侧重于验证脑机接口技术在治疗抑郁症方面的实际应用、疗效和安全性。 |