Decoding covert visual attention to motion direction using graph theory features of EEG signals and quadratic discriminant analysis by Zeinab Rezaei, Mohammad-Mahdi Mohammadi, Mohammad Reza Daliri

“…Pearson's correlation coefficient and coherence have been used to measure the functional connections between brain regions. In fact, each of these two criteria is regarded as an individual feature, and we perform decoding using each criterion separately. …”
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The emergence of visual category representations in infants’ brains by Xiaoqian Yan, Sarah Shi Tung, Bella Fascendini, Yulan Diana Chen, Anthony M Norcia, Kalanit Grill-Spector

“…Between 6 and 15 months response patterns become more distinct, such that a classifier can decode what an infant is looking at from their brain responses. …”
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Identification of the hemodynamic correlates of basic emotional states with a mobile functional near infrared spectroscopy system by Sinem Burcu Erdoğan

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Generative language reconstruction from brain recordings by Ziyi Ye, Qingyao Ai, Yiqun Liu, Maarten de Rijke, Min Zhang, Christina Lioma, Tuukka Ruotsalo

“…Existing research has addressed this challenge with a classification setup, where a set of language candidates are pre-constructed and then matched with the representation decoded from brain recordings. Here, we propose a method that addresses language reconstruction through auto-regressive generation, which directly uses the representation decoded from functional magnetic resonance imaging (fMRI) as the input for a large language model (LLM), mitigating the need for pre-constructed candidates. …”
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Clinical practice of Brain–Machine interfaces in neurological disorders by Kaishan Wang, Penghu Wei, Yongzhi Shan, Guoguang Zhao

“…By creating a direct communication interface between the brain and external devices, BMIs allow patients with severe neurological impairments to regain partial motor function, engage in nonverbal communication, and restore control over lost physiological functions. …”
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The Ethical Significance of Brain-Computer Interfaces as Enablers of Communication by Toma Gruica

“…Z., & Illes, J. (2022). Brain computer interfaces and communication disabilities: Ethical, legal, and social aspects of decoding speech from the brain. …”
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Stabilizing brain-computer interfaces through alignment of latent dynamics by Brianna M. Karpowicz, Yahia H. Ali, Lahiru N. Wimalasena, Andrew R. Sedler, Mohammad Reza Keshtkaran, Kevin Bodkin, Xuan Ma, Daniel B. Rubin, Ziv M. Williams, Sydney S. Cash, Leigh R. Hochberg, Lee E. Miller, Chethan Pandarinath

“…Abstract Intracortical brain-computer interfaces (iBCIs) restore motor function to people with paralysis by translating brain activity into control signals for external devices. …”
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Unsupervised method for representation transfer from one brain to another by Daiki Nakamura, Shizuo Kaji, Ryota Kanai, Ryusuke Hayashi

“…Although the anatomical arrangement of brain regions and the functional structures within them are similar across individuals, the representation of neural information, such as recorded brain activity, varies among individuals owing to various factors. …”
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VMD‐FBCCA classification method for SSVEP brain–computer interfaces by Ping Tan, Fengsheng Wang, Kaijun Zhou, Yi Shen

“…Abstract A steady‐state visually evoked potential (SSVEP) is a brain response to specific frequencies of visual stimuli, including their harmonic frequencies. …”
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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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Application of Brain-Computer Interfaces in Rehabilitation of Central Nervous System Disorders by GE Yi, ZHONG Ying, HONG Wenjun, XU Rong

“…Central nervous system (CNS) disorders often cause consciousness, motor, language, cognitive and sensory disorders, which seriously impair patients' quality of life. Brain-computer interface (BCI) technology provides a new paradigm for CNS functional rehabilitation through real-time decoding of brain intentions and transforming them into environmental interaction signals, and remodeling neuroplasticity through motor and sensory feedback and assisted rehabilitation training. …”
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The war inside your mind: unprotected brain battlefields and neuro-vulnerability by Robert McCreight

“…The engineered convergence of advanced technology such as cutting-edge medical technology frontiers of cognitive dynamics, decoding key neural functions, explaining brain biochemistry, and exploring excursions into neuromodulation and plasticity research make the brain a prime object of sustained scientific desire. …”
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Graphs Constructed from Instantaneous Amplitude and Phase of Electroencephalogram Successfully Differentiate Motor Imagery Tasks by Maliheh Miri, Vahid Abootalebi, Hamid Saeedi-Sourck, Dimitri Van De Ville, Hamid Behjat

“…Graph signal processing (GSP) has shown promising results in the analysis of brain imaging data. Methods: In this article, a GSP-based approach is presented that exploits instantaneous amplitude and phase coupling between EEG time series to decode motor imagery (MI) tasks. …”
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Neural signal analysis in chronic stroke: advancing intracortical brain-computer interface design by Nabila Shawki, Alessandro Napoli, Carlos E. Vargas-Irwin, Carlos E. Vargas-Irwin, Christopher K. Thompson, John P. Donoghue, John P. Donoghue, Mijail D. Serruya

“…Using several offline analysis techniques, we propose three features for decoding motor movements in stroke-affected brains.DiscussionDespite the presence of unique activities that were not reported in previous iBCI studies with intact brain functions, it is possible to decode motor intents from the neural signals collected from a subcortical stroke-affected brain.…”
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Fiber length distribution characterizes the brain network maturation during early school-age by Yanlin Yu, Qing Cai, Longnian Lin, Chu-Chung Huang

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Time-resolved connectivity reveals the “how” and “when” of brain networks reconfiguration during face processing by Antonio Maffei, Paola Sessa

“…Furthermore, using time-resolved decoding we observed that face familiarity can be distinguished very early on from the functional connectivity. …”
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Dance Training Shapes Action Perception and Its Neural Implementation within the Young and Older Adult Brain by Louise P. Kirsch, Nadine Diersch, Dilini K. Sumanapala, Emily S. Cross

“…Many factors influence brain responses when observing others in action, including training in a particular physical skill, such as sport or dance, and also general development and aging processes. …”
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Development and evaluation of a non-invasive brain-spine interface using transcutaneous spinal cord stimulation by Carolyn Atkinson, Lorenzo Lombardi, Meredith Lang, Rodolfo Keesey, Rachel Hawthorn, Zachary Seitz, Eric C. Leuthardt, Peter Brunner, Ismael Seáñez

“…Abstract Motor rehabilitation is a therapeutic process to facilitate functional recovery in people with spinal cord injury (SCI). …”
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Classification of left and right-hand motor imagery in acute stroke patients using EEG microstate by Shiyang Lv, Xiangying Ran, Mengsheng Xia, Yehong Zhang, Ting Pang, Xuezhi Zhou, Zongya Zhao, Yi Yu, Zhixian Gao

“…Abstract Background Stroke is one of the leading causes of adult disability, often resulting in motor dysfunction and brain network reorganization. Brain-computer interface (BCI) systems offer a novel approach to post-stroke motor rehabilitation, with motor imagery (MI) serving as a key paradigm that requires decoding left and right-hand MI differences to optimize system performance. …”
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Convolutional neural network (CNN) configuration using a learning automaton model for neonatal brain image segmentation. by Iran Sarafraz, Hamed Agahi, Azar Mahmoodzadeh

“…In this article, an adaptive method for CNN automatic configuration for neonatal brain image segmentation is presented based on the encoder-decoder structure, in which the hyperparameters of this network, i.e., size, length, and width of the filter in each layer along with the type of pooling functions with a reinforcement learning approach and an LA model are determined. …”
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