How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art
Objective. Processing strategies are analyzed with respect to the classification of
electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor …
electroencephalographic signals related to brain-computer interfaces (BCIs) based on motor …
A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface
Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of
communication through the utilization of neural activity generated due to kinesthetic …
communication through the utilization of neural activity generated due to kinesthetic …
SAE+ LSTM: A new framework for emotion recognition from multi-channel EEG
EEG-based automatic emotion recognition can help brain-inspired robots in improving their
interactions with humans. This paper presents a novel framework for emotion recognition …
interactions with humans. This paper presents a novel framework for emotion recognition …
Transformer-based spatial-temporal feature learning for EEG decoding
Y Song, X Jia, L Yang, L Xie - arXiv preprint arXiv:2106.11170, 2021 - arxiv.org
At present, people usually use some methods based on convolutional neural networks
(CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have limitations in …
(CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have limitations in …
Motor imagery EEG signals decoding by multivariate empirical wavelet transform-based framework for robust brain–computer interfaces
The robustness and computational load are the key challenges in motor imagery (MI) based
on electroencephalography (EEG) signals to decode for the development of practical brain …
on electroencephalography (EEG) signals to decode for the development of practical brain …
Personalized human activity recognition based on integrated wearable sensor and transfer learning
Human activity recognition (HAR) based on the wearable device has attracted more
attention from researchers with sensor technology development in recent years. However …
attention from researchers with sensor technology development in recent years. However …
Use of artificial intelligence techniques to assist individuals with physical disabilities
S Pancholi, JP Wachs… - Annual Review of …, 2024 - annualreviews.org
Assistive technologies (AT) enable people with disabilities to perform activities of daily living
more independently, have greater access to community and healthcare services, and be …
more independently, have greater access to community and healthcare services, and be …
Classification of EEG signals using Transformer based deep learning and ensemble models
Abstract A Brain-Computer Interface (BCI) is a communication and control system designed
to provide interaction between a user and a computer device. This interaction is based on …
to provide interaction between a user and a computer device. This interaction is based on …
Human machine interfaces in upper-limb prosthesis control: A survey of techniques for preprocessing and processing of biosignals
C Ahmadizadeh, M Khoshnam… - IEEE Signal Processing …, 2021 - ieeexplore.ieee.org
Prostheses provide a means for individuals with amputations to regain some of the lost
functions of their amputated limb. Human-machine interfaces (HMIs), used for controlling …
functions of their amputated limb. Human-machine interfaces (HMIs), used for controlling …
[HTML][HTML] Evaluation of machine learning algorithms for classification of EEG signals
In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the
accuracy of the classification of motor movements. Machine learning (ML) algorithms such …
accuracy of the classification of motor movements. Machine learning (ML) algorithms such …