arXiv:2609.36609v1 Announce Type: cross
Abstract: Electroencephalography (EEG) analysis requires careful choices in preprocessing, statistical modeling, and machine learning because EEG signals are h...
By Parsa Razmara, Woojae Jeong, Aditya Kommineni, Raymundo Cassani, Richard Leahy, Takfarinas Medani
arXiv:2608. 02083v1 Announce Type: new Abstract: Closed-loop lower-limb exoskeleton control via Electroencephalography (EEG) remains limited by motion artifacts, low signal-to-noise ratio, and binary gait formulations that fail to capture full cortical gait complexity.
By Shantanu Sarkar, Saurabh Prasad, Jose L. Contreras-Vidal
arXiv:2606. 20074v1 Announce Type: cross Abstract: Burst suppression (BS) is a clinically relevant electroencephalographic (EEG) pattern used to monitor sedation depth and brain activity in critically ill patients, particularly during induced coma in Intensive Care Units (ICUs).
By Elisa Vasta, Thorir Mar Ingolfsson, Andrea Cossettini, Luca Benini, Tilman Beck, Emanuela Keller, Una Pale
arXiv:2610.09792v1 Announce Type: new
Abstract: Electroencephalography (EEG) is highly susceptible to electromyographic (EMG) artifacts, whose temporal heterogeneity and spatial-spectral overlap with...
By Lu Wang-N\"oth, Hai Huang, Philipp Heiler, Shuqiong Wu, Liyun Zhang, Helmut Mayer
The paper presents a data‑driven framework that selects optimal EEG‑EMG channel pairs for hybrid brain‑computer interfaces by formulating the problem as a constrained bi‑objective optimisation. It maximises both the spatial relevance of EEG channels to motor cortex areas and the corticomuscular coupling strength, solved with NSGA‑II. Applied to motor‑imagery data from eight stroke patients, the method achieved an average classification accuracy of 89.6%, indicating improved capture of physiologically meaningful interactions.
By Dekka Muni Kumar, Yogesh Kumar Meena
arXiv:2606. 26519v1 Announce Type: new Abstract: Large language models (LLMs) can make scientific software easier to use.
By Zhiyuan Xu, Yueqing Dai, Junling Li, Junwen Luo