An analysis of deep learning models in SSVEP-based BCI: a survey
D Xu, F Tang, Y Li, Q Zhang, X Feng - Brain Sciences, 2023 - mdpi.com
The brain–computer interface (BCI), which provides a new way for humans to directly
communicate with robots without the involvement of the peripheral nervous system, has …
communicate with robots without the involvement of the peripheral nervous system, has …
A Survey of EEG and Machine Learning based methods for Neural Rehabilitation
One approach to therapy and training for the restoration of damaged muscles and motor
systems is rehabilitation. EEG-assisted Brain-Computer Interface (BCI) may assist in …
systems is rehabilitation. EEG-assisted Brain-Computer Interface (BCI) may assist in …
Correlation-filter-based channel and feature selection framework for hybrid EEG-fNIRS BCI applications
The proposed study is based on a feature and channel selection strategy that uses
correlation filters for brain–computer interface (BCI) applications using …
correlation filters for brain–computer interface (BCI) applications using …
[HTML][HTML] SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals
Deep learning has revolutionized EEG decoding, showcasing its ability to outperform
traditional machine learning models. However, unlike other fields, EEG decoding lacks …
traditional machine learning models. However, unlike other fields, EEG decoding lacks …
A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets
Parkinson's Disease (PD) is a prevalent neurodegenerative disorder with sig-nificant clinical
implications. Early and accurate diagnosis of PD is crucial for timely intervention and …
implications. Early and accurate diagnosis of PD is crucial for timely intervention and …
Qeegnet: Quantum machine learning for enhanced electroencephalography encoding
Electroencephalography (EEG) is a critical tool in neuroscience and clinical practice for
monitoring and analyzing brain activity. Traditional neural network models, such as EEG …
monitoring and analyzing brain activity. Traditional neural network models, such as EEG …
[HTML][HTML] An intrinsically explainable method to decode p300 waveforms from EEG signal plots based on convolutional neural networks
BE Ail, R Ramele, J Gambini, JM Santos - Brain Sciences, 2024 - mdpi.com
This work proposes an intrinsically explainable, straightforward method to decode P300
waveforms from electroencephalography (EEG) signals, overcoming the black box nature of …
waveforms from electroencephalography (EEG) signals, overcoming the black box nature of …
Recognition of map activities using eye tracking and EEG data
Recognizing the activities being performed on a map is crucial for adaptive map design
based on user context. Despite eye tracking (ET) demonstrating potential in recognizing …
based on user context. Despite eye tracking (ET) demonstrating potential in recognizing …
A continuous pursuit dataset for online deep learning-based EEG brain-computer interface
This dataset is from an EEG brain-computer interface (BCI) study investigating the use of
deep learning (DL) for online continuous pursuit (CP) BCI. In this task, subjects use Motor …
deep learning (DL) for online continuous pursuit (CP) BCI. In this task, subjects use Motor …
Mobile phone use driver distraction detection based on MSaE of multi-modality physiological signals
Driver distraction, a major cause of traffic crashes, is reported to reduce driving performance
and be detected with vehicle behavioral features. It also induces physiological responses …
and be detected with vehicle behavioral features. It also induces physiological responses …