EEG Signal Prediction for Motor Imagery Classification in Brain–Computer Interfaces by Óscar Wladimir Gómez-Morales, Diego Fabian Collazos-Huertas, Andrés Marino Álvarez-Meza, Cesar German Castellanos-Dominguez

“…Brain–computer interfaces (BCIs) based on motor imagery (MI) generally require EEG signals recorded from a large number of electrodes distributed across the cranial surface to achieve accurate MI classification. …”
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A Systematic Review of Bimanual Motor Coordination in Brain-Computer Interface by Poraneepan Tantawanich, Chatrin Phunruangsakao, Shin-Ichi Izumi, Mitsuhiro Hayashibe

“…Advancements in neuroscience and artificial intelligence are propelling rapid progress in brain-computer interfaces (BCIs). These developments hold significant potential for decoding motion intentions from brain signals, enabling direct control commands without reliance on conventional neural pathways. …”
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PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces. by Gursimran Singh, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Luca Longo

“…Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) have emerged as a transformative technology with applications spanning robotics, virtual reality, medicine, and rehabilitation. …”
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Dual-Mode Visual System for Brain–Computer Interfaces: Integrating SSVEP and P300 Responses by Ekgari Kasawala, Surej Mouli

“…In brain–computer interface (BCI) systems, steady-state visual-evoked potentials (SSVEP) and P300 responses have achieved widespread implementation owing to their superior information transfer rates (ITR) and minimal training requirements. …”
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A Review of Brain–Computer Interface-Based Language Decoding: From Signal Interpretation to Intelligent Communication by Yingyi Qiu, Han Liu, Mengyuan Zhao

“…Brain–computer interface (BCI) technologies for language decoding have emerged as a transformative bridge between neuroscience and artificial intelligence (AI), enabling direct neural–computational communication. …”
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A Spiking Neural Network With Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces by Peiliang Gong, Pengpai Wang, Yueying Zhou, Daoqiang Zhang

“…Electroencephalography (EEG) signals classification is essential for the brain-computer interface (BCI). Recently, energy-efficient spiking neural networks (SNNs) have shown great potential in EEG analysis due to their ability to capture the complex dynamic properties of biological neurons while also processing stimulus information through precisely timed spike trains. …”
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Integrating Brain-Computer Interface Systems into Occupational Therapy for Enhanced Independence of Stroke Patients: An Observational Study by Erika Endzelytė, Daiva Petruševičienė, Raimondas Kubilius, Sigitas Mingaila, Jolita Rapolienė, Inesa Rimdeikienė

“…<i>Background and Objectives</i>: Brain-computer interface (BCI) technology is revolutionizing stroke rehabilitation by offering innovative neuroengineering solutions to address neurological deficits. …”
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Factors influencing the social acceptance of brain-computer interface technology among Chinese general public: an exploratory study by RuiTong Xia, Shusheng Yang

“…This study investigates the impact of social factors on public acceptance of brain-computer interface (BCI) technology within China's general population. …”
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Efficacy of kinesthetic motor imagery based brain computer interface combined with tDCS on upper limb function in subacute stroke by Zhang Ming, Wu Yu, Jia Fan, Gao Ling, Chu Fengming, Tang Wei

“…Abstract This study investigates whether the combined effect of kinesthetic motor imagery-based brain computer interface (KI-BCI) and transcranial direct current stimulation (tDCS) on upper limb function in subacute stroke patients is more effective than using KI-BCI or tDCS alone. …”
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The application of vibration tactile stimulation in hand motor imagery paradigm: a pilot study by Wenbin Zhang, Aiguo Song, Hexuan Hu, Minmin Miao, Baoguo Xu

“…Background The motor imagery (MI) paradigm is widely used in active brain-computer interfaces (BCIs), but its effectiveness is hindered by accuracy limitations and individual variability. …”
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Performance Enhancement of Steady-State Visual Evoked Field-Based Brain&#x2013;Computer Interfaces Incorporating MEG Source Imaging by Ye-Sung Kim, Hyojeong Han, Cheong-Un Kim, Soo-In Choi, Min-Young Kim, Chang-Hwan Im

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Single-Source and Multi-Source Cross-Subject Transfer Based on Domain Adaptation Algorithms for EEG Classification by Rito Clifford Maswanganyi, Chunling Tu, Pius Adewale Owolawi, Shengzhi Du

“…Transfer learning (TL) has been employed in electroencephalogram (EEG)-based brain–computer interfaces (BCIs) to enhance performance for cross-session and cross-subject EEG classification. …”
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Effects and neural mechanisms of a brain–computer interface-controlled soft robotic glove on upper limb function in patients with subacute stroke: a randomized controlled fNIRS stu... by Xiang Ji, Xia Lu, Yi Xu, Wenbin Zhang, Han Yang, Chenghui Yin, Hewei Wang, Caili Ren, Yingying Ji, Yongqiang Li, Guilan Huang, Ying Shen

“…Abstract Background and purpose The brain-computer interface-based soft robotic glove (BCI-SRG) holds promise for upper limb rehabilitation in subacute stroke patients, yet its efficacy and neural mechanisms are unclear. …”
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Development of RelaxQuest: A Serious EEG-Controlled Game Designed to Promote Relaxation and Self-Regulation with a Potential Focus on ADHD Intervention by Alan F. Pérez Vidal, José-Antonio Cervantes, Jesse Y. Rumbo-Morales, Felipe D. J. Sorcia-Vázquez, Gerardo Ortiz-Torres, Christian A. Castro Moncada, Ignacio de la Torre Arias

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Optimization of EEG-based wheelchair control: machine learning, feature selection, outlier management, and explainable AI by Amr M. Hamed, Abdel-Fattah Attia, Heba El-Behery

“…This approach, complemented by explainability techniques, offers a robust solution for EEG-based wheelchair control systems and paves the way for interpretable brain-computer interfaces (BCIs).…”
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Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods by Zhishui You, Yuzhu Guo, Xiulei Zhang, Yifan Zhao

“…Driven by the remarkable capabilities of machine learning, brain–computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. …”
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Variation-resilient spike-timing-dependent plasticity in memristors using bursting neuron circuit by Yize Liu, Jiayi Zhao, Yu Xiao, Peng Chen, Haisong Chen, Enhui He, Peng Lin, Gang Pan

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Modified stereotactic neurosurgery techniques for rodent surgery enhance survival and reduce surgery time in a severe traumatic brain injury model by Pongsakorn Wechakarn, Sumeth Klomchitcharoen, Tirapoot Jatupornpoonsub, Nantawachara Jirakittayakorn, Chanon Puttanawarut, Wanlop Likitsuntonwong, Khuanjit Chaimongkolnukul, Yodchanan Wongsawat

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Electroencephalographic differences between waking and sleeping periods in patients with prolonged disorders of consciousness at different levels of consciousness by Keke Li, Man Li, Wanqing Liu, Wanqing Liu, Yanzhi Wu, Fang Li, Jingwei Xie, Jingwei Xie, Jingwei Xie, Shaolong Zhou, Shaolong Zhou, Shaolong Zhou, Sen Wang, Yongkun Guo, Yongkun Guo, Yongkun Guo, Jiahui Pan, Xinjun Wang, Xinjun Wang, Xinjun Wang, Xinjun Wang

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Establishing a social behavior paradigm for female mice by Hanyang Xiao, Hanyang Xiao, Hanyang Xiao, Changgang Huang, Changgang Huang, Changgang Huang, Yue Wu, Yue Wu, Yue Wu, Jacob Junlin Wang, Hao Wang, Hao Wang, Hao Wang, Hao Wang

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