[go: up one dir, main page]

Li, 2020 - Google Patents

Electroencephalography signal analysis and classification based on deep learning

Li, 2020

Document ID
15039291907218187047
Author
Li Z
Publication year
Publication venue
2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)

External Links

Snippet

Brain computer interface (BCI) bridges the interaction between the brain activities and an external machines/device. Electroencephalography (EEG) has its advantages over other brainwave monitoring tools in cost, portability and monitoring frequency and accuracy. The …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0476Electroencephalography

Similar Documents

Publication Publication Date Title
CN113627518B (en) Method for realizing neural network brain electricity emotion recognition model by utilizing transfer learning
Khan et al. Focal onset seizure prediction using convolutional networks
Zheng et al. Time-frequency analysis of scalp EEG with Hilbert-Huang transform and deep learning
Atangana et al. EEG signal classification using LDA and MLP classifier
Abdelhameed et al. An efficient deep learning system for epileptic seizure prediction
Qian et al. Decision-level fusion of EEG and pupil features for single-trial visual detection analysis
Patel et al. CNN-FEBAC: A framework for attention measurement of autistic individuals
Qin et al. Patient-specific seizure prediction with scalp EEG using convolutional neural network and extreme learning machine
Rauf et al. Toward improved classification of perceived stress using time domain features
Li Electroencephalography signal analysis and classification based on deep learning
Sharifi et al. Autism Diagnosis from EEG Signals Using Machine Learning Algorithms and Convolutional Neural Networks
Ranjan et al. A machine learning framework for automatic diagnosis of schizophrenia using EEG signals
Ren et al. Extracting and supplementing method for EEG signal in manufacturing workshop based on deep learning of time–frequency correlation
Said et al. Automatic detection of mild cognitive impairment from EEG recordings using discrete wavelet transform leader and ensemble learning methods
Priya et al. A Hybrid Neural Network Approach Based on RNN and CNN for the Detection of Major Depressive Disorder
Sharma et al. Detection of Schizophrenia using Machine Learning
Saroja et al. Classification of mild cognitive impairment with deep transfer learning approach using CWT based scalogram images
Annapoorani et al. ACNN-LSTM: A Novel Deep Learning Approach for Decoding Depression from Resting-State EEG.
Kotwal et al. EEG-based emotion recognition: Leveraging CNNs for precision
Asayesh et al. Transfer learning using deep convolutional neural network for predicting dementia severity
Dentamaro et al. An Approach using transformer architecture for emotion recognition through Electrocardiogram Signal (s).
Leelavathi et al. EEG-Based Seizure Prediction Using CNN and BiLSTM Algorithm of Deep Learning
Srinath et al. Epilepsy Disease Detection Using the Proposed CNN-FCM Approach
Dhake et al. Implemented OBL-DE assisted Tasmanian devil optimisation for selecting the optimal features using EEG signal for stress detection
Bala et al. Efficient Epileptic Seizure Recognition System using the Multi-model Ensemble Method from EEG