Demirel et al., 2024 - Google Patents

Harmonic enhancement to optimize EOG based ocular activity decoding: A hybrid approach with harmonic source separation and EEMD

Demirel et al., 2024

View HTML @Full View
Document ID
7944065743909169013
Author
Demirel
Reguş L
Köse H
Publication year
Publication venue
Heliyon

External Links

Snippet

Intelligent robotic systems for patients with motor impairments have gained significant interest over the past few years. Various sensor types and human-machine interface (HMI) methods have been developed; however, most research in this area has focused on eye …
Continue reading at www.cell.com (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
    • 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
    • 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
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • 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
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • 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

Similar Documents

Publication Publication Date Title
Guerrero et al. EEG signal analysis using classification techniques: Logistic regression, artificial neural networks, support vector machines, and convolutional neural networks
Padfield et al. EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges
Singh et al. Trends in EEG signal feature extraction applications
Hasnul et al. Electrocardiogram-based emotion recognition systems and their applications in healthcare—a review
Balam et al. Automated classification system for drowsiness detection using convolutional neural network and electroencephalogram
Buettner et al. High-performance detection of epilepsy in seizure-free EEG recordings: A novel machine learning approach using very specific epileptic EEG sub-bands
Martin et al. Word pair classification during imagined speech using direct brain recordings
Shen et al. A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network
Khan et al. A machine learning based depression screening framework using temporal domain features of the electroencephalography signals
US20170202518A1 (en) System and method for brain state classification
Herbozo Contreras et al. KAN–EEG: towards replacing backbone–MLP for an effective seizure detection system
Seo et al. EEG-based emotion classification for Alzheimer’s disease patients using conventional machine learning and recurrent neural network models
Siuly et al. SchizoGoogLeNet: The GoogLeNet‐Based Deep Feature Extraction Design for Automatic Detection of Schizophrenia
Mendoza-Palechor et al. Affective recognition from EEG signals: an integrated data-mining approach
Bhadra et al. Multiview EEG signal analysis for diagnosis of schizophrenia: an optimized deep learning approach
Khanam et al. Electroencephalogram-based cognitive load level classification using wavelet decomposition and support vector machine
Pirasteh et al. EEG-based brain-computer interface methods with the aim of rehabilitating advanced stage ALS patients
Awais et al. Effective connectivity for decoding electroencephalographic motor imagery using a probabilistic neural network
Pinto et al. Comprehensive review of depression detection techniques based on machine learning approach
Mouazen et al. Enhancing EEG-based emotion detection with hybrid models: Insights from DEAP dataset applications
Faghfouri et al. A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment
Batistić et al. Motor imagery classification based on eeg sensing with visual and vibrotactile guidance
Ganiga et al. Modeling EEG signals for mental confusion using DNN and LSTM with custom attention layer
Al Mazroa et al. EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection
Lu et al. Identification of auditory object-specific attention from single-trial electroencephalogram signals via entropy measures and machine learning