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 …
[HTML][HTML] Application of deep learning models for automated identification of Parkinson's disease: A review (2011–2021)
Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting
over 6 million people globally. Although there are symptomatic treatments that can increase …
over 6 million people globally. Although there are symptomatic treatments that can increase …
[HTML][HTML] Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques
Early detection of Parkinson's disease (PD) is very important in clinical diagnosis for
preventing disease development. In this study, we present efficient discrete wavelet …
preventing disease development. In this study, we present efficient discrete wavelet …
[HTML][HTML] Parkinson's disease detection from resting-state EEG signals using common spatial pattern, entropy, and machine learning techniques
Parkinson's disease (PD) is a very common brain abnormality that affects people all over the
world. Early detection of such abnormality is critical in clinical diagnosis in order to prevent …
world. Early detection of such abnormality is critical in clinical diagnosis in order to prevent …
EEG-GCN: spatio-temporal and self-adaptive graph convolutional networks for single and multi-view EEG-based emotion recognition
Y Gao, X Fu, T Ouyang, Y Wang - IEEE Signal Processing …, 2022 - ieeexplore.ieee.org
Graph networks are naturally suitable for modeling multi-channel features of EEG signals.
However, the existing study that attempts to utilize graph-based neural networks for EEG …
However, the existing study that attempts to utilize graph-based neural networks for EEG …
EEG-Based Parkinson's Disease Recognition Via Attention-based Sparse Graph Convolutional Neural Network
Parkinson's disease (PD) is a complicated neurological ailment that affects both the physical
and mental wellness of elderly individuals which makes it problematic to diagnose in its …
and mental wellness of elderly individuals which makes it problematic to diagnose in its …
[HTML][HTML] Survey of machine learning techniques in the analysis of EEG signals for Parkinson's disease: A systematic review
Background: Parkinson's disease (PD) affects 7–10 million people worldwide. Its diagnosis
is clinical and can be supported by image-based tests, which are expensive and not always …
is clinical and can be supported by image-based tests, which are expensive and not always …
[HTML][HTML] An interpretable model based on graph learning for diagnosis of Parkinson's disease with voice-related EEG
S Zhao, G Dai, J Li, X Zhu, X Huang, Y Li, M Tan… - NPJ Digital …, 2024 - nature.com
Parkinson's disease (PD) exhibits significant clinical heterogeneity, presenting challenges in
the identification of reliable electroencephalogram (EEG) biomarkers. Machine learning …
the identification of reliable electroencephalogram (EEG) biomarkers. Machine learning …
[HTML][HTML] The applied principles of EEG analysis methods in neuroscience and clinical neurology
H Zhang, QQ Zhou, H Chen, XQ Hu, WG Li, Y Bai… - Military Medical …, 2023 - Springer
Electroencephalography (EEG) is a non-invasive measurement method for brain activity.
Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural …
Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural …
[HTML][HTML] CNN architectures and feature extraction methods for EEG imaginary speech recognition
Speech is a complex mechanism allowing us to communicate our needs, desires and
thoughts. In some cases of neural dysfunctions, this ability is highly affected, which makes …
thoughts. In some cases of neural dysfunctions, this ability is highly affected, which makes …