A Personalized Multimodal BCI–Soft Robotics System for Rehabilitating Upper Limb Function in Chronic Stroke Patients by Brian Premchand, Zhuo Zhang, Kai Keng Ang, Juanhong Yu, Isaac Okumura Tan, Josephine Pei Wen Lam, Anna Xin Yi Choo, Ananda Sidarta, Patrick Wai Hang Kwong, Lau Ha Chloe Chung

“…Multimodal brain–computer interfaces (BCIs) that combine electrical features from electroencephalography (EEG) and hemodynamic features from functional near-infrared spectroscopy (fNIRS) have the potential to improve performance. …”
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Human-Intelligent Information System Collaboration in Digital Twin Environment: Value Proposition, Key Technologies, and Practical Approaches by Fangrui BAI, Shaobo LIANG, Dan WU, Yuheng REN, Fan YANG

“…The supporting technologies in digital twin environment mainly involve data acquisition and data transmission, model twin mainly lies in data fusion and management, image recognition and processing, and process twin mainly involves human-computer interfaces, immersive perception and other key technologies. …”
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Biomechanics characterization of an implantable ultrathin intracortical electrode through finite element method by Linsen Peng, Longchun Wang, Shuhui Wu, Mingcheng Gu, Shang Deng, Jingquan Liu, Cheng-kung Cheng, Xiaohong Sui

“…Abstract Neural electrodes are widely used in brain-computer interfaces and neuroprosthesis for the treatment of various neurological disorders. …”
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The Next Frontier in Brain Monitoring: A Comprehensive Look at In-Ear EEG Electrodes and Their Applications by Alexandra Stefania Mihai (Ungureanu), Oana Geman, Roxana Toderean, Lucas Miron, Sara SharghiLavan

“…The integration of intra-auricular EEG in wearable devices opens new perspectives for clinical applications, including sleep monitoring, epilepsy diagnosis, and brain–computer interfaces. This study highlights the challenges and prospects in the development of in-ear EEG electrodes, with a focus on integration into wearable devices and the use of biocompatible materials to improve durability and enhance user comfort. …”
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Optimizing a Bayesian Method for Estimating the Hurst Exponent in Behavioral Sciences by Madhur Mangalam, Taylor J. Wilson, Joel H. Sommerfeld, Aaron D. Likens

“…The computation time increases exponentially with the time series length <i>N</i>, easily exceeding several hours for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>=</mo><mn>1024</mn></mrow></semantics></math></inline-formula>, limiting the utility of the HK method in real-time paradigms, such as biofeedback and brain–computer interfaces. To address this issue, we have provided data on the estimation accuracy of the Hurst exponent <i>H</i> for synthetic time series as a function of a priori known values of <i>H</i>, the time series length, and the simulated sample size from the posterior distribution <i>n</i>—a critical step in the Bayesian estimation method. …”
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