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Zhang H, Jiao L, Yang S, Li H, Jiang X, Feng J, Zou S, Xu Q, Gu J, Wang X, Wei B. Brain-computer interfaces: the innovative key to unlocking neurological conditions. Int J Surg 2024; 110:5745-5762. [PMID: 39166947 PMCID: PMC11392146 DOI: 10.1097/js9.0000000000002022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 07/30/2024] [Indexed: 08/23/2024]
Abstract
Neurological disorders such as Parkinson's disease, stroke, and spinal cord injury can pose significant threats to human mortality, morbidity, and functional independence. Brain-Computer Interface (BCI) technology, which facilitates direct communication between the brain and external devices, emerges as an innovative key to unlocking neurological conditions, demonstrating significant promise in this context. This comprehensive review uniquely synthesizes the latest advancements in BCI research across multiple neurological disorders, offering an interdisciplinary perspective on both clinical applications and emerging technologies. We explore the progress in BCI research and its applications in addressing various neurological conditions, with a particular focus on recent clinical studies and prospective developments. Initially, the review provides an up-to-date overview of BCI technology, encompassing its classification, operational principles, and prevalent paradigms. It then critically examines specific BCI applications in movement disorders, disorders of consciousness, cognitive and mental disorders, as well as sensory disorders, highlighting novel approaches and their potential impact on patient care. This review reveals emerging trends in BCI applications, such as the integration of artificial intelligence and the development of closed-loop systems, which represent significant advancements over previous technologies. The review concludes by discussing the prospects and directions of BCI technology, underscoring the need for interdisciplinary collaboration and ethical considerations. It emphasizes the importance of prioritizing bidirectional and high-performance BCIs, areas that have been underexplored in previous reviews. Additionally, we identify crucial gaps in current research, particularly in long-term clinical efficacy and the need for standardized protocols. The role of neurosurgery in spearheading the clinical translation of BCI research is highlighted. Our comprehensive analysis presents BCI technology as an innovative key to unlocking neurological disorders, offering a transformative approach to diagnosing, treating, and rehabilitating neurological conditions, with substantial potential to enhance patients' quality of life and advance the field of neurotechnology.
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Affiliation(s)
- Hongyu Zhang
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Le Jiao
- Department of Neurosurgery, The First Hospital of Qiqihar, Qiqihar, Heilongjiang Province
| | | | | | | | - Jing Feng
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Shuhuai Zou
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Qiang Xu
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Jianheng Gu
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
- Harbin Medical University, Harbin
| | - Xuefeng Wang
- Department of Neurosurgery, The Fourth Affiliated Hospital of Harbin Medical University
| | - Baojian Wei
- School of Nursing, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, Shandong, People's Republic of China
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Liu Y, Wang N, Su X, Zhao T, Zhang J, Geng Y, Wang N, Zhou M, Zhang G, Huang L. Classification of cognitive impairment in older adults based on brain functional state measurement data via hierarchical clustering analysis. Front Aging Neurosci 2023; 15:1198481. [PMID: 38161594 PMCID: PMC10757366 DOI: 10.3389/fnagi.2023.1198481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 11/24/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction Cognitive impairment (CI) is a common degenerative condition in the older population. However, the current methods for assessing CI are not based on brain functional state, which leads to delayed diagnosis, limiting the initiatives towards achieving early interventions. Methods A total of one hundred and forty-nine community-dwelling older adults were recruited. Montreal Cognitive Assessment (MoCA) and Mini-Mental State Exam (MMSE) were used to screen for CI, while brain functional was assessed by brain functional state measurement (BFSM) based on electroencephalogram. Bain functional state indicators associated with CI were selected by lasso and logistic regression models (LRM). We then classified the CI participants based on the selected variables using hierarchical clustering analysis. Results Eighty-one participants with CI detected by MoCA were divided into five groups. Cluster 1 had relatively lower brain functional states. Cluster 2 had highest mental task-switching index (MTSi, 13.7 ± 3.4), Cluster 3 had the highest sensory threshold index (STi, 29.9 ± 7.7), Cluster 4 had high mental fatigue index (MFi) and cluster 5 had the highest mental refractory period index (MRPi), and external apprehension index (EAi) (21.6 ± 4.4, 35.4 ± 17.7, respectively). Thirty-three participants with CI detected by MMSE were divided into 3 categories. Cluster 1 had the highest introspective intensity index (IIi, 63.4 ± 20.0), anxiety tendency index (ATi, 67.2 ± 13.6), emotional resistance index (ERi, 50.2 ± 11.9), and hypoxia index (Hi, 41.8 ± 8.3). Cluster 2 had the highest implicit cognitive threshold index (ICTi, 87.2 ± 12.7), and cognitive efficiency index (CEi, 213.8 ± 72.0). Cluster 3 had higher STi. The classifications both showed well intra-group consistency and inter-group variability. Conclusion In our study, BFSM-based classification can be used to identify clinically and brain-functionally relevant CI subtypes, by which clinicians can perform personalized early rehabilitation.
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Affiliation(s)
- Yangxiaoxue Liu
- Medical School of Chinese PLA, Beijing, China
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
- School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, China
| | - Na Wang
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xinling Su
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Tianshu Zhao
- Medical School of Chinese PLA, Beijing, China
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
- School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, China
| | - Jiali Zhang
- Medical School of Chinese PLA, Beijing, China
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
- School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, China
| | - Yuhan Geng
- Medical School of Chinese PLA, Beijing, China
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Ning Wang
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Ming Zhou
- Medical School of Chinese PLA, Beijing, China
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Gongzi Zhang
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Liping Huang
- Department of Rehabilitation Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China
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