ArticleSensors (Basel, Switzerland)2019
A New Approach for Motor Imagery Classification Based on Sorted Blind Source Separation, Continuous Wavelet Transform, and Convolutional Neural Network.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Parallel Ictal-Net, a Parallel CNN Architecture with Efficient Channel Attention for Seizure Detection.Sensors (Basel, Switzerland) · 2024Article
- EEG Network Analysis of Depressive Emotion Interference Spatial Cognition Based on a Simulated Robotic Arm Docking Task.Brain sciences · 2023Article
- Ensemble Wavelet Decomposition-Based Detection of Mental States Using Electroencephalography Signals.Sensors (Basel, Switzerland) · 2023Article
- A Quantitative Investigation of Mental Fatigue Elicited during Motor Imagery Practice: Selective Effects on Maximal Force Performance and Imagery Ability.Brain sciences · 2023Article
- A convolutional recurrent neural network with attention for response prediction to repetitive transcranial magnetic stimulation in major depressive disorder.Scientific reports · 2023Article
- An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey.Brain sciences · 2023Review
- Multi-domain feature joint optimization based on multi-view learning for improving the EEG decoding.Frontiers in human neuroscience · 2023Article
- Modeling Brain Functional Connectivity Patterns during an Isometric Arm Force Exertion Task at Different Levels of Perceived Exertion: A Graph Theoretical Approach.Brain sciences · 2022Article
- Graph Theoretical Analysis of EEG Functional Connectivity Patterns and Fusion with Physiological Signals for Emotion Recognition.Sensors (Basel, Switzerland) · 2022Article
- Responsive Neurostimulation for Seizure Control: Current Status and Future Directions.Biomedicines · 2022Review
- Wearable Sensor-Based Human Activity Recognition with Transformer Model.Sensors (Basel, Switzerland) · 2022Article
- Multi-Hierarchical Fusion to Capture the Latent Invariance for Calibration-Free Brain-Computer Interfaces.Frontiers in neuroscience · 2022Article
- Similarities and disparities between visual analysis and high-resolution electromyography of facial expressions.PloS one · 2022Article
- A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition.Sensors (Basel, Switzerland) · 2021Article
- Sensorless Estimation Based on Neural Networks Trained with the Dynamic Response Points.Sensors (Basel, Switzerland) · 2021Article
- A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface.Sensors (Basel, Switzerland) · 2021Review
- Deep Feature MiningFrontiers in bioengineering and biotechnology · 2021Article
- The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN.PeerJ. Computer science · 2021Article
- Sensors, Signal and Image Processing in Biomedicine and Assisted Living.Sensors (Basel, Switzerland) · 2020Article
- CNN-based framework using spatial dropping for enhanced interpretation of neural activity in motor imagery classification.Brain informatics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-Computer Interfaces (BCI) are systems that allow the interaction of people and devices on the grounds of brain activity. The noninvasive and most viable way to obtain such information is by using electroencephalography (EEG). However, these signals have a low signal-to-noise ratio, as well as a low spatial resolution. This work proposes a new method built from the combination of a Blind Source Separation (BSS) to obtain estimated independent components, a 2D representation of these component signals using the Continuous Wavelet Transform (CWT), and a classification stage using a Convolutional Neural Network (CNN) approach. A criterion based on the spectral correlation with a Movement Related Independent Component (MRIC) is used to sort the estimated sources by BSS, thus reducing the spatial variance. The experimental results of 94.66% using a k-fold cross validation are competitive with techniques recently reported in the state-of-the-art.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.