Li et al., 2021 - Google Patents
A novel spatio-temporal field for emotion recognition based on EEG signalsLi et al., 2021
- Document ID
- 5575281875082270670
- Author
- Li W
- Zhang Z
- Hou B
- Li X
- Publication year
- Publication venue
- IEEE Sensors Journal
External Links
Snippet
Electroencephalogram (EEG) sensor data contain rich information about human emotionality. Emotion recognition based on EEG signals has attracted growing attention of researchers, especially with the fast progress of intelligent sensing technology. Numerous …
- 241000997494 Oneirodidae 0 abstract description 21
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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
-
- 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
- A61B5/72—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
-
- 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
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
-
- 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
- A61B5/0452—Detecting specific parameters of the electrocardiograph cycle
-
- 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
- A61B5/165—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
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
-
- 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
- A61B5/164—Lie 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
- A61B5/05—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
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Lashgari et al. | Data augmentation for deep-learning-based electroencephalography | |
Pandey et al. | Subject independent emotion recognition from EEG using VMD and deep learning | |
Liu et al. | Subject-independent emotion recognition of EEG signals based on dynamic empirical convolutional neural network | |
Zhang et al. | Respiration-based emotion recognition with deep learning | |
Kiranyaz et al. | Automated patient-specific classification of long-term electroencephalography | |
Li et al. | A novel ensemble learning method using multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition | |
Nath et al. | An efficient approach to eeg-based emotion recognition using lstm network | |
Li et al. | A novel spatio-temporal field for emotion recognition based on EEG signals | |
Soni et al. | Graphical representation learning-based approach for automatic classification of electroencephalogram signals in depression | |
Chandra et al. | Comparative study of physiological signals from empatica e4 wristband for stress classification | |
Zhang et al. | A comparison study on multidomain EEG features for sleep stage classification | |
Alshamrani | An advanced stress detection approach based on processing data from wearable wrist devices | |
Chen et al. | An effective entropy-assisted mind-wandering detection system using EEG signals of MM-SART database | |
Zheng et al. | A power spectrum pattern difference-based time-frequency sub-band selection method for MI-EEG classification | |
Elahi et al. | Estimation of hypnosis susceptibility based on electroencephalogram signal features | |
Wang et al. | Deep learning for single-channel EEG signals sleep stage scoring based on frequency domain representation | |
Gupta et al. | Emotion recognition during social interactions using peripheral physiological signals | |
Zhong et al. | A sleep stage classification algorithm of wearable system based on multiscale residual convolutional neural network | |
Kant et al. | Transfer learning-based EEG analysis of visual attention and working memory on motor cortex for BCI | |
Can | Stressed or just running? Differentiation of mental stress and physical activityby using machine learning | |
Gupta et al. | Contemporary Intelligent Technologies for Electroencephalogram-Based Brain Computer Interface | |
Lahane | Brain computer interfaces techniques for stress management | |
Pusarla et al. | Electroencephalogram-Based Emotion Recognition Using Random Forest | |
Ahmed et al. | Improving emotion detection through artificial intelligence from eeg brainwave signals | |
Wu et al. | Cognitive and Emotional Monitoring with Inexpensive Wrist-Worn Consumer-Grade Wearables |