[PDF][PDF] A review of processing methods and classification algorithm for EEG signal
The analysis technique of EEG signals is developing promptly with the evolution of Brain
Computer-Interfaces science. The processing and classification algorithm of EEG signals …
Computer-Interfaces science. The processing and classification algorithm of EEG signals …
Time window and frequency band optimization using regularized neighbourhood component analysis for Multi-View Motor Imagery EEG classification
Spatial features optimized at frequency bands have been widely used in motor imagery (MI)
based brain-computer interface (BCI) systems. However, using a fixed time window of …
based brain-computer interface (BCI) systems. However, using a fixed time window of …
A survey of analysis and classification of EEG signals for brain-computer interfaces
A Brain Computer-Interfaces (BCI) is a communication system that enables human brain to
interact with machines or devices without involving physical contact by using EEG signals …
interact with machines or devices without involving physical contact by using EEG signals …
Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques
Objective. Accurate decoding of individual finger movements is crucial for advanced
prosthetic control. In this work, we introduce the use of Riemannian-space features and …
prosthetic control. In this work, we introduce the use of Riemannian-space features and …
Time-series discrimination using feature relevance analysis in motor imagery classification
AM Álvarez-Meza, LF Velasquez-Martinez… - Neurocomputing, 2015 - Elsevier
The use of motor imagery discrimination using feature relevance analysis (MIDFR) is
investigated for classification tasks based on electroencephalography (EEG) signals. The …
investigated for classification tasks based on electroencephalography (EEG) signals. The …
Recognition of words from brain-generated signals of speech-impaired people: Application of autoencoders as a neural Turing machine controller in deep neural …
B Boloukian, F Safi-Esfahani - Neural Networks, 2020 - Elsevier
There is an essential requirement to support people with speech and communication
disabilities. A brain–computer interface using electroencephalography (EEG) is applied to …
disabilities. A brain–computer interface using electroencephalography (EEG) is applied to …
[HTML][HTML] Enhancing Motor Imagery Classification in Brain–Computer Interfaces Using Deep Learning and Continuous Wavelet Transform
In brain–computer interface (BCI) systems, motor imagery (MI) electroencephalogram (EEG)
is widely used to interpret the human brain. However, MI classification is challenging due to …
is widely used to interpret the human brain. However, MI classification is challenging due to …
Motor imagery classification using feature relevance analysis: An Emotiv-based BCI system
J Hurtado-Rincon, S Rojas-Jaramillo… - … XIX Symposium on …, 2014 - ieeexplore.ieee.org
Brain Computer Interfaces (BCI) have been emerged as an alternative to support automatic
systems able to interpret brain functions, commonly, by analyzing electroencephalography …
systems able to interpret brain functions, commonly, by analyzing electroencephalography …
Decoding of imaginary motor movements of fists applying spatial filtering in a BCI simulated application
J Boelts, A Cerquera, AF Ruiz-Olaya - … IWINAC 2015, Elche, Spain, June 1 …, 2015 - Springer
This work presents a study that evaluates different scenarios of preprocessing and
processing of EEG registers, with the aim to predict fist imaginary movements utilizing the …
processing of EEG registers, with the aim to predict fist imaginary movements utilizing the …
Electroencephalogram and Electrocardiogram in Human-Computer Interaction
P Li, Y Qian, N Si - 2022 IEEE 2nd International Conference on …, 2022 - ieeexplore.ieee.org
Electroencephalogram (EEG) and Electrocardiogram (ECG) have been widely used in
clinical diagnosis and have shown their potential in Human-Computer Interaction (HCI) …
clinical diagnosis and have shown their potential in Human-Computer Interaction (HCI) …