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

Sburlea et al., 2017 - Google Patents

Advantages of EEG phase patterns for the detection of gait intention in healthy and stroke subjects

Sburlea et al., 2017

View PDF
Document ID
643721568243908753
Author
Sburlea A
Montesano L
Minguez J
Publication year
Publication venue
Journal of neural engineering

External Links

Snippet

Objective. One use of EEG-based brain–computer interfaces (BCIs) in rehabilitation is the detection of movement intention. In this paper we investigate for the first time the instantaneous phase of movement related cortical potential (MRCP) and its application to …
Continue reading at arxiv.org (PDF) (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
    • 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
    • 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
    • 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
    • A61B5/48—Other medical applications
    • 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
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • 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
    • 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
    • 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
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00268—Feature extraction; Face representation
    • G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships

Similar Documents

Publication Publication Date Title
Sburlea et al. Advantages of EEG phase patterns for the detection of gait intention in healthy and stroke subjects
Ofner et al. Upper limb movements can be decoded from the time-domain of low-frequency EEG
Arpaia et al. How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art
Bulea et al. Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution
Jochumsen et al. Detection and classification of movement-related cortical potentials associated with task force and speed
Gwin et al. Removal of movement artifact from high-density EEG recorded during walking and running
Kumar et al. CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI
Garipelli et al. Single trial analysis of slow cortical potentials: a study on anticipation related potentials
Ibáñez et al. Detection of the onset of upper-limb movements based on the combined analysis of changes in the sensorimotor rhythms and slow cortical potentials
Guarnieri et al. Online EEG artifact removal for BCI applications by adaptive spatial filtering
Vučković et al. A two-stage four-class BCI based on imaginary movements of the left and the right wrist
Rodríguez-Ugarte et al. Personalized offline and pseudo-online BCI models to detect pedaling intent
Prashant et al. Brain computer interface: A review
Yang et al. Detection of motor imagery of swallow EEG signals based on the dual-tree complex wavelet transform and adaptive model selection
Rozado et al. Improving the performance of an EEG-based motor imagery brain computer interface using task evoked changes in pupil diameter
Sosnik et al. Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG slow cortical potentials
Handiru et al. EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement
Gupta et al. Detecting eye movements in EEG for controlling devices
Liu et al. Brain-actuated gait trainer with visual and proprioceptive feedback
Guerrero-Mendez et al. EEG motor imagery classification using deep learning approaches in naïve BCI users
Sosnik et al. Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG current source dipoles
Jochumsen et al. Movement intention detection in adolescents with cerebral palsy from single-trial EEG
Korik et al. 3D hand motion trajectory prediction from EEG mu and beta bandpower
Yousefi et al. Exploiting error-related potentials in cognitive task based BCI
Tariq et al. Classification of left and right foot kinaesthetic motor imagery using common spatial pattern