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

Masuda et al., 2023 - Google Patents

Multi-Input CNN-LSTM deep learning model for fear level classification based on EEG and peripheral physiological signals

Masuda et al., 2023

View HTML
Document ID
13733999482345025744
Author
Masuda N
Yairi I
Publication year
Publication venue
Frontiers in Psychology

External Links

Snippet

Objective and accurate classification of fear levels is a socially important task that contributes to developing treatments for Anxiety Disorder, Obsessive–compulsive Disorder, Post-Traumatic Stress Disorder (PTSD), and Phobia. This study examines a deep learning …
Continue reading at www.frontiersin.org (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-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
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • 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
    • 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
    • A61B5/16—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • 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
    • A61B5/16—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
    • A61B5/168—Evaluating attention deficit, hyperactivity
    • 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
    • A61B5/0402—Electrocardiography, i.e. ECG
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
    • 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
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
    • G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection

Similar Documents

Publication Publication Date Title
Dadebayev et al. EEG-based emotion recognition: Review of commercial EEG devices and machine learning techniques
Halim et al. On identification of driving-induced stress using electroencephalogram signals: A framework based on wearable safety-critical scheme and machine learning
Bălan et al. An investigation of various machine and deep learning techniques applied in automatic fear level detection and acrophobia virtual therapy
Schmidt et al. Wearable affect and stress recognition: A review
Lin et al. Review of studies on emotion recognition and judgment based on physiological signals
Fritz et al. Leveraging biometric data to boost software developer productivity
Saffaryazdi et al. Using facial micro-expressions in combination with EEG and physiological signals for emotion recognition
Seo et al. Machine learning approaches for boredom classification using EEG
Masood et al. Investigating EEG patterns for dual-stimuli induced human fear emotional state
Li et al. Brain-based computer interfaces in virtual reality
Seal et al. An EEG database and its initial benchmark emotion classification performance
Masuda et al. Multi-Input CNN-LSTM deep learning model for fear level classification based on EEG and peripheral physiological signals
Rajwal et al. Convolutional neural network-based EEG signal analysis: A systematic review
Pan et al. ST-SCGNN: a spatio-temporal self-constructing graph neural network for cross-subject EEG-based emotion recognition and consciousness detection
Georgieva et al. Learning to decode human emotions from event-related potentials
Sakalle et al. Emotion recognition using portable eeg device
Samal et al. Role of machine learning and deep learning techniques in EEG-based BCI emotion recognition system: a review
Bhatti et al. Attx: Attentive cross-connections for fusion of wearable signals in emotion recognition
Mateos-García et al. Driver Stress Detection from Physiological Signals by Virtual Reality Simulator
Nakagome et al. Deep learning methods for EEG neural classification
Wardoyo et al. Oversampling approach using radius-SMOTE for imbalance electroencephalography datasets
KR20220060976A (en) Deep Learning Method and Apparatus for Emotion Recognition based on Efficient Multimodal Feature Groups and Model Selection
Ismail et al. A systematic review of emotion recognition using cardio-based signals
Blackmore et al. Integrating Biofeedback and Artificial Intelligence into eXtended Reality Training Scenarios: A Systematic Literature Review
Bhanumathi et al. Feedback Artificial Shuffled Shepherd Optimization‐Based Deep Maxout Network for Human Emotion Recognition Using EEG Signals