[go: up one dir, main page]

Wang et al., 2023 - Google Patents

A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface

Wang et al., 2023

View HTML
Document ID
16907719358934922810
Author
Wang P
Cao X
Zhou Y
Gong P
Yousefnezhad M
Shao W
Zhang D
Publication year
Publication venue
Frontiers in neuroscience

External Links

Snippet

The advance in neuroscience and computer technology over the past decades have made brain-computer interface (BCI) a most promising area of neurorehabilitation and neurophysiology research. Limb motion decoding has gradually become a hot topic in the …
Continue reading at www.frontiersin.org (HTML) (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
    • 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
    • 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
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0488—Electromyography
    • 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
    • G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
    • 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
    • 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
    • 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
    • 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
    • A61B5/103—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
    • A61B5/05—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

Similar Documents

Publication Publication Date Title
Li et al. Gesture recognition using surface electromyography and deep learning for prostheses hand: state-of-the-art, challenges, and future
Zhang et al. Learning effective spatial–temporal features for sEMG armband-based gesture recognition
Liu et al. Wrist angle prediction under different loads based on GA‐ELM neural network and surface electromyography
Wang et al. A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface
Ullah et al. Imagined character recognition through EEG signals using deep convolutional neural network
Ma et al. sEMG-based trunk compensation detection in rehabilitation training
CN113520413B (en) Lower limb multi-joint angle estimation method based on surface electromyogram signals
Shao et al. Single-channel SEMG using wavelet deep belief networks for upper limb motion recognition
Kalagi et al. Brain computer interface systems using non-invasive electroencephalogram signal: A literature review
Kuang et al. Extreme learning machine classification method for lower limb movement recognition
Yu et al. Deep neural network-based empirical mode decomposition for motor imagery EEG classification
Scano et al. Robotic assistance for upper limbs may induce slight changes in motor modules compared with free movements in stroke survivors: a cluster-based muscle synergy analysis
Yu et al. Wrist torque estimation via electromyographic motor unit decomposition and image reconstruction
Korik et al. Decoding imagined 3D arm movement trajectories from EEG to control two virtual arms—a pilot study
Kansal et al. DL-AMPUT-EEG: Design and development of the low-cost prosthesis for rehabilitation of upper limb amputees using deep-learning-based techniques
Zeng et al. The advantage of low-delta electroencephalogram phase feature for reconstructing the center-out reaching hand movements
Wang et al. Neural decoding of Chinese sign language with machine learning for brain–computer interfaces
Kumar et al. Classification of error-related potentials evoked during stroke rehabilitation training
Karrenbach et al. Deep learning and session-specific rapid recalibration for dynamic hand gesture recognition from EMG
Xu et al. Electroencephalogram source imaging and brain network based natural grasps decoding
Xu et al. Natural grasping movement recognition and force estimation using electromyography
Khattak et al. Hand gesture recognition with deep residual network using Semg signal
Xiong et al. Single-trial recognition of imagined forces and speeds of hand clenching based on brain topography and brain network
Wang et al. Using non-linear dynamics of EEG signals to classify primary hand movement intent under opposite hand movement
Syed et al. Deep learning-based framework for real-time upper limb motion intention classification using combined bio-signals