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Scientific Reports, 2023 Recent advancements in machine learning and deep learning (DL) based neural decoders have significantly improved decoding capabilities using scalp electroencephalography (EEG). However, the interpretability of DL models remains an under-explored area. In
Akshay Sujatha Ravindran +1 more
doaj +1 more source
Data-driven multivariate and multiscale methods for brain computer interface [PDF]
, 2012 This thesis focuses on the development of data-driven multivariate and multiscale methods for brain computer interface (BCI) systems. The electroencephalogram (EEG), the most convenient means to measure neurophysiological activity due to its ...
Park, Cheolsoo, Park, Cheolsoo
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Generative Adversarial Networks-Based Data Augmentation for Brain–Computer Interface
IEEE Transactions on Neural Networks and Learning Systems, 2020 The performance of a classifier in a brain–computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled ...
Fatemeh Fahimi +4 more
semanticscholar +1 more source
Sub-band common spatial pattern (SBCSP) for brain-computer interface [PDF]
, 2007 Brain-computer interface (BCI) is a system to translate humans thoughts into commands. For electroencephalography (EEG) based BCI, motor imagery is considered as one of the most effective ways.
Cuntai, Guan +3 more
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Progress in Brain Computer Interface: Challenges and Opportunities [PDF]
Frontiers in Systems Neuroscience, 2019 Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have
S. Saha +7 more
semanticscholar +1 more source
Is implicit motor imagery a reliable strategy for a brain computer interface? [PDF]
, 2017 Explicit motor imagery (eMI) is a widely used brain computer interface (BCI) paradigm, but not everybody can accomplish this task. Here we propose a BCI based on implicit motor imagery (iMI).
Osuagwu, Bethel A. +2 more
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Brain Neuroplasticity Leveraging Virtual Reality and Brain–Computer Interface Technologies
Italian National Conference on SensorsThis study explores neuroplasticity through the use of virtual reality (VR) and brain–computer interfaces (BCIs). Neuroplasticity is the brain’s ability to reorganize itself by forming new neural connections in response to learning, experience, and ...
Athanasios Drigas, Angeliki Sideraki
semanticscholar +1 more source
Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review
Frontiers in Computational Neuroscience, 2020 Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the neurophysiological processes underpinning the SMR often vary over time and across subjects ...
S. Saha, M. Baumert
semanticscholar +1 more source
Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation
Frontiers in Neuroscience, 2021 Brain-computer interfaces (BCIs) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the ...
C. Simon +4 more
semanticscholar +1 more source
Personalized Brain-Computer Interface Models for Motor Rehabilitation
, 2017 We propose to fuse two currently separate research lines on novel therapies for stroke rehabilitation: brain-computer interface (BCI) training and transcranial electrical stimulation (TES).
Grosse-Wentrup, Moritz +5 more
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