[HTML][HTML] Cognitive neuroscience and robotics: Advancements and future research directions
In recent years, brain-based technologies that capitalise on human abilities to facilitate
human–system/robot interactions have been actively explored, especially in brain robotics …
human–system/robot interactions have been actively explored, especially in brain robotics …
Status of deep learning for EEG-based brain–computer interface applications
In the previous decade, breakthroughs in the central nervous system bioinformatics and
computational innovation have prompted significant developments in brain–computer …
computational innovation have prompted significant developments in brain–computer …
A systematic comparison of deep learning methods for EEG time series analysis
D Walther, J Viehweg, J Haueisen… - Frontiers in …, 2023 - frontiersin.org
Analyzing time series data like EEG or MEG is challenging due to noisy, high-dimensional,
and patient-specific signals. Deep learning methods have been demonstrated to be superior …
and patient-specific signals. Deep learning methods have been demonstrated to be superior …
Survey on the research direction of EEG-based signal processing
C Sun, C Mou - Frontiers in Neuroscience, 2023 - frontiersin.org
Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI)
systems due to its portability and simplicity. In this paper, we provide a comprehensive …
systems due to its portability and simplicity. In this paper, we provide a comprehensive …
Adaptive spatiotemporal encoding network for cognitive assessment using resting state EEG
Cognitive impairment, marked by neurodegenerative damage, leads to diminished cognitive
function decline. Accurate cognitive assessment is crucial for early detection and progress …
function decline. Accurate cognitive assessment is crucial for early detection and progress …
Compact convolutional transformer for subject-independent motor imagery EEG-based BCIs
A Keutayeva, N Fakhrutdinov, B Abibullaev - Scientific Reports, 2024 - nature.com
Motor imagery electroencephalography (EEG) analysis is crucial for the development of
effective brain-computer interfaces (BCIs), yet it presents considerable challenges due to the …
effective brain-computer interfaces (BCIs), yet it presents considerable challenges due to the …
Identification of perceived sentences using deep neural networks in EEG
Objetive. Decoding speech from brain activity can enable communication for individuals with
speech disorders. Deep neural networks (DNNs) have shown great potential for speech …
speech disorders. Deep neural networks (DNNs) have shown great potential for speech …
A deep neural network and transfer learning combined method for cross-task classification of error-related potentials
Background Error-related potentials (ErrPs) are electrophysiological responses that
naturally occur when humans perceive wrongdoing or encounter unexpected events. It …
naturally occur when humans perceive wrongdoing or encounter unexpected events. It …
[HTML][HTML] A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks
P Ma, C Dong, R Lin, H Liu, D Lei, X Chen… - Frontiers in …, 2024 - frontiersin.org
Background The development of Brain-Computer Interface (BCI) technology has brought
tremendous potential to various fields. In recent years, prominent research has focused on …
tremendous potential to various fields. In recent years, prominent research has focused on …
Enhancing Classification Accuracy with Integrated Contextual Gate Network: Deep Learning Approach for Functional Near-Infrared Spectroscopy Brain–Computer …
Brain–computer interface (BCI) systems include signal acquisition, preprocessing, feature
extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning …
extraction, classification, and an application phase. In fNIRS-BCI systems, deep learning …