Nothing Special   »   [go: up one dir, main page]

Abdullah et al., 2018 - Google Patents

EEG Emotion Detection Using Multi-Model Classification

Abdullah et al., 2018

Document ID
11368919764242296521
Author
Abdullah M
Christensen L
Publication year
Publication venue
2018 International Conference on Bioinformatics and Systems Biology (BSB)

External Links

Snippet

In the field of Human-Machine Interaction HMI identifying the human emotion is an important input for the machines to better enhance the communication. In this paper, we identifying 10 different emotions based on Valance, Arousal and Dominance using five different models …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • A61B5/0484—Electroencephalography using evoked response
    • 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
    • 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
    • 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
    • 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
    • A61B5/16—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
    • A61B5/164—Lie detection
    • 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
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • 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
    • A61B5/0488—Electromyography
    • 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
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0531—Measuring skin impedance
    • 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

Similar Documents

Publication Publication Date Title
Yuen et al. Classification of human emotions from EEG signals using statistical features and neural network
CN110070105B (en) Electroencephalogram emotion recognition method and system based on meta-learning example rapid screening
CN111714118B (en) Brain cognition model fusion method based on ensemble learning
Jeevan et al. EEG-based emotion recognition using LSTM-RNN machine learning algorithm
Zhu et al. EEG-based emotion recognition using discriminative graph regularized extreme learning machine
Ahire et al. A comprehensive review of machine learning approaches for dyslexia diagnosis
Yudhana et al. Human emotion recognition based on EEG signal using fast fourier transform and K-Nearest neighbor
Vallabhaneni et al. Deep learning algorithms in eeg signal decoding application: a review
Amin et al. A wearable exam stress dataset for predicting grades using physiological signals
Hosseini et al. Deep convolutional neural network for automated detection of mind wandering using EEG signals
CN111772629B (en) Brain cognitive skill transplanting method
Qu Time Continuity Voting for Electroencephalography (EEG) Classification
Gupta et al. A machine learning-based decision support system for temporal human cognitive state estimation during online education using wearable physiological monitoring devices
Khoshnevis et al. Diagnosis of Parkinson’s disease using higher order statistical analysis of alpha and beta rhythms
Parmar et al. A novel and efficient Wavelet Scattering Transform approach for primitive-stage dyslexia-detection using electroencephalogram signals
Jawed et al. Deep learning-based assessment model for Real-time identification of visual learners using Raw EEG
Taherisadr et al. Erudite: Human-in-the-loop iot for an adaptive personalized learning system
Abdullah et al. EEG Emotion Detection Using Multi-Model Classification
Shekokar et al. Epileptic seizure classification using LSTM
Makkar et al. EEG Signal Processing and Feature Extraction
Zubair et al. Early detection on autistic children by using EEG signals
Christensen et al. Novel Model Architecture for EEG Emotion Classification
Wu et al. Mental fatigue assessment by an arbitrary channel EEG based on morphological features and LSTM-CNN
Verma et al. AttentioNet: Monitoring Student Attention Type in Learning with EEG-Based Measurement System
Ashraf Kiyani et al. A comprehensive review on sentiment perception using electroencephalography (EEG)