Boubchir et al., 2015 - Google Patents

EEG error potentials detection and classification using time-frequency features for robot reinforcement learning

Boubchir et al., 2015

Document ID
18169849763662115590
Author
Boubchir L
Touati Y
Daachi B
Chérif A
Publication year
Publication venue
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

External Links

Snippet

In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials (SSVEP) techniques …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0476Electroencephalography
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235Details of waveform analysis
    • A61B5/7264Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0402Electrocardiography, i.e. ECG
    • A61B5/0452Detecting specific parameters of the electrocardiograph cycle
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0488Electromyography
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/40Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076Diagnosing or monitoring particular conditions of the nervous system
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/16Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/05Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radiowaves
    • A61B5/053Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0531Measuring skin impedance
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/48Other medical applications

Similar Documents

Publication Publication Date Title
Mazher et al. An EEG-based cognitive load assessment in multimedia learning using feature extraction and partial directed coherence
Benalcázar et al. Hand gesture recognition using machine learning and the Myo armband
Tiwari et al. MIDNN-a classification approach for the EEG based motor imagery tasks using deep neural network
Chatterjee et al. EEG based motor imagery classification using SVM and MLP
Alomari et al. Automated classification of L/R hand movement EEG signals using advanced feature extraction and machine learning
Mohanchandra et al. A communication paradigm using subvocalized speech: translating brain signals into speech
Boubchir et al. EEG error potentials detection and classification using time-frequency features for robot reinforcement learning
US20210100663A1 (en) Mixed variable decoding for neural prosthetics
Uyulan et al. Analysis of Time—Frequency EEG feature extraction methods for mental task classification
Bousseta et al. EEG efficient classification of imagined hand movement using RBF kernel SVM
Fatima et al. Towards a low cost Brain-computer Interface for real time control of a 2 DOF robotic arm
Padmavathi et al. A review on EEG based brain computer interface systems
Nouri et al. A new approach to feature extraction in MI-based BCI systems
Ghane et al. Robust understanding of EEG patterns in silent speech
Begum et al. EEG based patient monitoring system for mental alertness using adaptive neuro-fuzzy approach
Ramakrishna et al. Classification of human emotions using EEG-based causal connectivity patterns
Aswinseshadri et al. Feature selection in brain computer interface using genetics method
Kaur et al. Technology development for unblessed people using bci: A survey
Szczuko Rough set-based classification of EEG signals related to real and imagery motion
Pisarchik et al. Development of intelligent system for classification of multiple human brain states corresponding to different real and imaginary movements
Dzitac et al. Identification of ERD using fuzzy inference systems for brain-computer interface
Kundra et al. Classification of EEG based diseases using data mining
Kumari et al. Application of empirical mode decomposition for feature extraction from EEG signals
Karam et al. Neural network for recognition of brain wave signals
Gupta et al. A three phase approach for mental task classification using EEG