[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Zhou et al., 2020 - Google Patents

MI-EEG temporal information learning based on one-dimensional convolutional neural network

Zhou et al., 2020

Document ID
4544647317875162403
Author
Zhou Y
Ouyang R
Wu X
Publication year
Publication venue
2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC)

External Links

Snippet

The Brain-Computer Interface (BCI) is a special communication system that does not rely on the nerve and muscle tissue around the human body. it realizes the direct communication between the brain and the computer. The key to the realization of the BCI system is …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0476—Electroencephalography
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
    • G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
    • G06K9/4619—Biologically-inspired filters, e.g. receptive fields
    • G06K9/4623—Biologically-inspired filters, e.g. receptive fields with interaction between the responses of different filters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches

Similar Documents

Publication Publication Date Title
Dose et al. An end-to-end deep learning approach to MI-EEG signal classification for BCIs
Ma et al. Deep channel-correlation network for motor imagery decoding from the same limb
Hermosilla et al. Shallow convolutional network excel for classifying motor imagery EEG in BCI applications
Raza et al. Single-trial EEG classification with EEGNet and neural structured learning for improving BCI performance
Cheng et al. Exploring motor imagery EEG patterns for stroke patients with deep neural networks
Ma et al. A novel hybrid CNN-transformer model for EEG motor imagery classification
Wei et al. Motor imagery EEG signal classification based on deep transfer learning
Jia et al. Joint spatial and temporal features extraction for multi-classification of motor imagery EEG
Liu et al. Uncorrelated multiway discriminant analysis for motor imagery EEG classification
Zhang et al. An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding
Zhou et al. Speech2eeg: Leveraging pretrained speech model for eeg signal recognition
Elessawy et al. A long short-term memory autoencoder approach for EEG motor imagery classification
Zhang et al. A computationally efficient multiclass time-frequency common spatial pattern analysis on EEG motor imagery
Cai et al. Motor imagery decoding in the presence of distraction using graph sequence neural networks
Bhalerao et al. Automatic detection of motor imagery EEG signals using swarm decomposition for robust BCI systems
Mirzabagherian et al. Temporal-spatial convolutional residual network for decoding attempted movement related EEG signals of subjects with spinal cord injury
Shepelev et al. A novel neural network approach to creating a brain–computer interface based on the EEG patterns of voluntary muscle movements
Tang et al. A novel classification algorithm for MI-EEG based on deep learning
Arı et al. NF-EEG: A generalized CNN model for multi class EEG motor imagery classification without signal preprocessing for brain computer interfaces
Cososchi et al. EEG features extraction for motor imagery
Zhou et al. MI-EEG temporal information learning based on one-dimensional convolutional neural network
Sridhar et al. A Neural Network Approach for EEG classification in BCI
Ananthi et al. A review on-EEG signals by motor imagery based brain computer interface
Jiang et al. EEG-based hand motion pattern recognition using deep learning network algorithms
Arabshahi et al. A convolutional neural network and stacked autoencoders approach for motor imagery based brain-computer interface