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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Preliminary evaluation of the FastCAP for users of the Nurotron cochlear implant by Xue-Ying Yang, Sui Huang, Qian-Jie Fu, John Galvin, Bing Chen, Ji-Sheng Liu, Duo-Duo Tao

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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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Does music counteract mental fatigue? A systematic review. by Cong Ding, Soh Kim Geok, He Sun, Samsilah Roslan, Shudian Cao, Yue Zhao

“…The physiological marker of steady-state visually evoked potential-based brain-computer interface (SSVEP-BCI) amplitude increased, confirming that exciting music counteracts mental fatigue more effectively than relaxing music. …”
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Global glucose metabolism rate as diagnostic marker for disorder of consciousness of patients: quantitative FDG-PET study by Dongsheng Liu, Dongsheng Liu, Dongsheng Liu, Nan Wang, Nan Wang, Nan Wang, Ming Song, Ming Song, Ming Song, Xiaoke Chai, Xiaoke Chai, Qiheng He, Tianqing Cao, Dawei Kong, Zhuhuan Song, Guangming Zhang, Lei Liu, Xiaosong Wang, Guoqiang Chen, Shaoya Yin, Shaoya Yin, Shaoya Yin, Yi Yang, Yi Yang, Yi Yang, Yi Yang, Yi Yang, Yi Yang, Yi Yang, Yi Yang, Jizong Zhao, Jizong Zhao

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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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Patterned electrical brain stimulation by a wireless network of implantable microdevices by Ah-Hyoung Lee, Jihun Lee, Vincent Leung, Lawrence Larson, Arto Nurmikko

“…Abstract Transmitting meaningful information into brain circuits by electronic means is a challenge facing brain-computer interfaces. A key goal is to find an approach to inject spatially structured local current stimuli across swaths of sensory areas of the cortex. …”
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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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Bioaugmented design and functional evaluation of low damage implantable array electrodes by Ling Wang, Chenrui Zhang, Zhiyan Hao, Siqi Yao, Luge Bai, Joaquim Miguel Oliveira, Pan Wang, Kun Zhang, Chen Zhang, Jiankang He, Rui L. Reis, Dichen Li

“…Implantable neural electrodes are key components of brain-computer interfaces (BCI), but the mismatch in mechanical and biological properties between electrode materials and brain tissue can lead to foreign body reactions and glial scarring, and subsequently compromise the long-term stability of electrical signal transmission. …”
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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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Managing ADHD Symptoms in Children Through the Use of Various Technology-Driven Serious Games: A Systematic Review by Aikaterini Doulou, Pantelis Pergantis, Athanasios Drigas, Charalampos Skianis

“…This research investigates using PC, mobile/tablet applications, augmented reality, virtual reality, and brain–computer interfaces to develop executive functions and metacognitive and emotional competencies in children with ADHD through serious games. …”
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Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study by Rishishankar E. Suresh, M S Zobaer, Matthew J. Triano, Brian F. Saway, Parneet Grewal, Nathan C. Rowland

“…These results suggest their feasibility for real-time movement detection in neurorehabilitation, including brain–computer interfaces for stroke recovery.…”
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