EEG classification of motor imagery using a novel deep learning framework
M Dai, D Zheng, R Na, S Wang, S Zhang - Sensors, 2019 - mdpi.com
Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI)
are still limited. In this paper, we propose a classification framework for MI …
are still limited. In this paper, we propose a classification framework for MI …
Subject-independent brain–computer interfaces based on deep convolutional neural networks
For a brain-computer interface (BCI) system, a calibration procedure is required for each
individual user before he/she can use the BCI. This procedure requires approximately 20-30 …
individual user before he/she can use the BCI. This procedure requires approximately 20-30 …
Motor imagery EEG spectral-spatial feature optimization using dual-tree complex wavelet and neighbourhood component analysis
Background Frequency band optimization improves the performance of common spatial
pattern (CSP) in motor imagery (MI) tasks classification because MI-related …
pattern (CSP) in motor imagery (MI) tasks classification because MI-related …
Spatial‐Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network
M Miao, W Hu, H Yin, K Zhang - … and mathematical methods in …, 2020 - Wiley Online Library
EEG pattern recognition is an important part of motor imagery‐(MI‐) based brain computer
interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two …
interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two …
Densely feature fusion based on convolutional neural networks for motor imagery EEG classification
D Li, J Wang, J Xu, X Fang - IEEE Access, 2019 - ieeexplore.ieee.org
Electroencephalogram (EEG) signals have been used in the Brain-computer interface (BCI)
technology to implement direct communication between the human body and the outside …
technology to implement direct communication between the human body and the outside …
Non-homogeneous spatial filter optimization for ElectroEncephaloGram (EEG)-based motor imagery classification
Neuronal power attenuation or enhancement in specific frequency bands over the
sensorimotor cortex, called Event-Related Desynchronization (ERD) or Event-Related …
sensorimotor cortex, called Event-Related Desynchronization (ERD) or Event-Related …
Learning EEG topographical representation for classification via convolutional neural network
Electroencephalography (EEG) topographical representation (ETR) can monitor regional
brain activities and is emerging as a successful technique for causally exploring cortical …
brain activities and is emerging as a successful technique for causally exploring cortical …
Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework
Identifying motor and mental imagery electroencephalography (EEG) signals is imperative to
realizing automated, robust brain-computer interface (BCI) systems. In the present study, we …
realizing automated, robust brain-computer interface (BCI) systems. In the present study, we …
Separable common spatio-spectral patterns for motor imagery BCI systems
AS Aghaei, MS Mahanta… - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
Objective: Feature extraction is one of the most important steps in any brain-computer
interface (BCI) system. In particular, spatio-spectral feature extraction for motor-imagery BCIs …
interface (BCI) system. In particular, spatio-spectral feature extraction for motor-imagery BCIs …
Investigating feature selection techniques to enhance the performance of EEG-based motor imagery tasks classification
Analyzing electroencephalography (EEG) signals with machine learning approaches has
become an attractive research domain for linking the brain to the outside world to establish …
become an attractive research domain for linking the brain to the outside world to establish …