Oligodendrocytes drive neuroinflammation and neurodegeneration in Parkinson’s disease via the prosaposin-GPR37-IL-6 axis by Qiang Ma, Jin-Lan Tian, Yao Lou, Ran Guo, Xiao-Ru Ma, Jian-Bin Wu, Jing Yang, Bing-Jie Tang, Shun Li, Mengsheng Qiu, Shumin Duan, Jing-Wei Zhao, Jing Zhang, Zhen-Zhong Xu

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Neurofilament light chain levels in neuronal surface antibody-associated autoimmune encephalitis: a systematic review and meta-analysis by Qi-Lun Lai, Meng-Ting Cai, Er-Chuang Li, Gao-Li Fang, Chun-Hong Shen, Yong-Feng Xu, Song Qiao, Jia-Jia Wang, Qin-Jie Weng, Yin-Xi Zhang

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Comparing P300 flashing paradigms in online typing with language models. by Nand Chandravadia, Shrita Pendekanti, Dustin Roberts, Robert Tran, Saarang Panchavati, Corey Arnold, Nader Pouratian, William Speier

“…The P300 Speller is a brain-computer interface system that allows victims of motor neuron diseases to regain the ability to communicate by typing characters into a computer by thought. …”
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Teleoperation Robot Control of a Hybrid EEG-Based BCI Arm Manipulator Using ROS by Vidya Nandikolla, Daniel A. Medina Portilla

“…To create the environment and user interface, a robot operating system (ROS) is used. Live brain computer interface (BCI) commands from a trained user are successfully harvested and used as an input signal to pick a goal point from 3D point cloud data and calculate the goal position of the robots’ mobile base, placing the goal point in the robot arms workspace. …”
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Research on How Human Intelligence, Consciousness, and Cognitive Computing Affect the Development of Artificial Intelligence by Yanyan Dong, Jie Hou, Ning Zhang, Maocong Zhang

“…In the future, the research and development of cutting-edge technologies such as brain-computer interface (BCI) together with the development of the human brain will eventually usher in a strong AI era, when AI can simulate and replace human’s imagination, emotion, intuition, potential, tacit knowledge, and other kinds of personalized intelligence. …”
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Real-Time Classification of Deep and Non-Deep Sleep With Comparative Intervention Experiments by Mo Xia, Hongxi Xue, Boning Li, Jianting Cao

“…This paper presents a system that utilizes a Brain-Computer Interface and a Deep Learning Network for the real-time classification of non-deep sleep and deep sleep. …”
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Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI by Tae-Ju Lee, Seung-Min Park, Kwee-Bo Sim

“…This paper presents a heuristic method for electroencephalography (EEG) grouping and feature classification using harmony search (HS) for improving the accuracy of the brain-computer interface (BCI) system. EEG, a noninvasive BCI method, uses many electrodes on the scalp, and a large number of electrodes make the resulting analysis difficult. …”
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A method of EEG signal feature extraction based on hybrid DWT and EMD by Xiaozhong Geng, Linen Wang, Ping Yu, Weixin Hu, Qipeng Liang, Xintong Zhang, Cheng Chen, Xi Zhang

“…The processing and recognition of electroencephalogram (EEG) signal is the most important part of brain-computer interface (BCI) system, and the quality of signal processing and recognition is directly related to the effectiveness of BCI system. …”
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Neuromodulation: clinical advances and future perspectives by ZHANG Jian⁃guo, XIE Hu⁃tao, YANG An⁃chao

“…Additionally, it explores the integration trend between neuromodulation and brain⁃computer interface (BCI), pointing out that closed⁃loop neuromodulation has become an important component of BCI, providing new approaches for precise treatment and individualized modulation. …”
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Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble by Xianglong Zhu, Ming Meng, Zewen Yan, Zhizeng Luo

“…Background: Decoding motor intentions from electroencephalogram (EEG) signals is a critical component of motor imagery-based brain–computer interface (MI–BCIs). In traditional EEG signal classification, effectively utilizing the valuable information contained within the electroencephalogram is crucial. …”
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BCI-Based Rehabilitation on the Stroke in Sequela Stage by Yangyang Miao, Shugeng Chen, Xinru Zhang, Jing Jin, Ren Xu, Ian Daly, Jie Jia, Xingyu Wang, Andrzej Cichocki, Tzyy-Ping Jung

“…Studies have shown that motor imagery- (MI-) based brain-computer interface (BCI) has a positive effect on poststroke rehabilitation. …”
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Reward signals in the motor cortex: from biology to neurotechnology by Gerard Derosiere, Solaiman Shokur, Pierre Vassiliadis

“…In this Perspective, we highlight the functional roles of M1 reward signals and propose how they could guide advances in neurotechnologies for movement restoration, specifically brain-computer interfaces and non-invasive brain stimulation. …”
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Seven Capital Devices for the Future of Stroke Rehabilitation by M. Iosa, G. Morone, A. Fusco, M. Bragoni, P. Coiro, M. Multari, V. Venturiero, D. De Angelis, L. Pratesi, S. Paolucci

“…In this paper, we have taken into account seven promising technologies that can improve rehabilitation of patients with stroke in the early future: (1) robotic devices for lower and upper limb recovery, (2) brain computer interfaces, (3) noninvasive brain stimulators, (4) neuroprostheses, (5) wearable devices for quantitative human movement analysis, (6) virtual reality, and (7) tablet-pc used for neurorehabilitation.…”
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A Task-Related EEG Microstate Clustering Algorithm Based on Spatial Patterns, Riemannian Distance, and a Deep Autoencoder by Shihao Pan, Tongyuan Shen, Yongxiang Lian, Li Shi

“…The task-related EEG was extensively analyzed in the field of brain–computer interfaces (BCIs); however, its primary objective is classification rather than segmentation. …”
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onEEGwaveLAD: A fully automated online EEG wavelet-based learning adaptive denoiser for artefacts identification and mitigation. by Luca Longo, Richard B Reilly

“…With the popularity of Brain-Computer Interfaces and the application of Electroencephalography in daily activities and other ecological settings, there is an increasing need for robust, online, near real-time denoising techniques, without additional reference signals, that is fully automated and does not require human supervision nor multi-channel information. …”
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The role of fMRI in the mind decoding process in adults: a systematic review by Sahal Alotaibi, Maher Mohammed Alotaibi, Faisal Saleh Alghamdi, Mishaal Abdullah Alshehri, Khaled Majed Bamusa, Ziyad Faiz Almalki, Sultan Alamri, Ahmad Joman Alghamdi, Mohammed Alhazmi, Hamid Osman, Mayeen U. Khandaker

“…Studies were selected based on strict inclusion and exclusion criteria: peer-reviewed; published between 2000 and 2024 (in English); focused on adults; investigated mind-reading (mental state decoding, brain-computer interfaces) or related processes; and employed various mind-reading techniques (pattern classification, multivariate analysis, decoding algorithms). …”
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Harnessing the Multi-Phasal Nature of Speech-EEG for Enhancing Imagined Speech Recognition by Rini Sharon, Mriganka Sur, Hema Murthy

“…Analyzing speech-electroencephalogram (EEG) is pivotal for developing non-invasive and naturalistic brain-computer interfaces. Recognizing that the nature of human communication involves multiple phases like audition, imagination, articulation, and production, this study uncovers the shared cognitive imprints that represent speech cognition across these phases. …”
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A Lightweight Network with Domain Adaptation for Motor Imagery Recognition by Xinmin Ding, Zenghui Zhang, Kun Wang, Xiaolin Xiao, Minpeng Xu

“…Brain–computer interfaces (BCI) are an effective tool for recognizing motor imagery and have been widely applied in the motor control and assistive operation domains. …”
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EEG-Triggered Functional Electrical Stimulation Therapy for Restoring Upper Limb Function in Chronic Stroke with Severe Hemiplegia by Cesar Marquez-Chin, Aaron Marquis, Milos R. Popovic

“…We report the therapeutic effects of integrating brain-computer interfacing technology and functional electrical stimulation therapy to restore upper limb reaching movements in a 64-year-old man with severe left hemiplegia following a hemorrhagic stroke he sustained six years prior to this study. …”
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Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders by Souhaila Khalfallah, William Puech, Mehdi Tlija, Kais Bouallegue

“…In conclusion, this research highlights the effectiveness of ML and DL techniques in EEG signal processing, offering valuable contributions to the field of brain-computer interfaces and advancing the potential for more accurate neurological disease classification and diagnosis.…”
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