[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Bhalerao et al., 2023 - Google Patents

Automatic detection of motor imagery EEG signals using swarm decomposition for robust BCI systems

Bhalerao et al., 2023

View PDF
Document ID
10611426096344680538
Author
Bhalerao S
Pachori R
Publication year
Publication venue
Human-Machine Interface Technology Advancements and Applications

External Links

Snippet

The development of brain–computer interface (BCI) systems faces a major challenge in achieving a reliable classification of motor imagery-based electroencephalogram signals (MI- EEG). The MI-EEG signals consist of nonstationary multicomponent modulation and …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • 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
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component 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
    • A61B5/72—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
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • 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
    • 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
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7232—Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period
    • 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
    • G06K9/00496—Recognising patterns in signals and combinations thereof
    • 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

Similar Documents

Publication Publication Date Title
Autthasan et al. MIN2Net: End-to-end multi-task learning for subject-independent motor imagery EEG classification
Al-Saegh et al. Deep learning for motor imagery EEG-based classification: A review
Zhao et al. A multi-branch 3D convolutional neural network for EEG-based motor imagery classification
Benalcázar et al. Hand gesture recognition using machine learning and the Myo armband
Aggarwal et al. Signal processing techniques for motor imagery brain computer interface: A review
Bentlemsan et al. Random forest and filter bank common spatial patterns for EEG-based motor imagery classification
Jingwei et al. Deep learning EEG response representation for brain computer interface
Bhalerao et al. Automatic detection of motor imagery EEG signals using swarm decomposition for robust BCI systems
CN110555468A (en) Electroencephalogram signal identification method and system combining recursion graph and CNN
Tang et al. Motor imagery EEG decoding based on multi-scale hybrid networks and feature enhancement
Li et al. EEG signal classification method based on feature priority analysis and CNN
Paranjape et al. Cross-correlation aided ensemble of classifiers for BCI oriented EEG study
Serdar Bascil et al. Multi-channel EEG signal feature extraction and pattern recognition on horizontal mental imagination task of 1-D cursor movement for brain computer interface
Abibullaev et al. A brute-force CNN model selection for accurate classification of sensorimotor rhythms in BCIs
Subasi et al. The Ensemble Machine Learning‐Based Classification of Motor Imagery Tasks in Brain‐Computer Interface
Wang et al. Multiband decomposition and spectral discriminative analysis for motor imagery BCI via deep neural network
Zhang et al. An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding
Li et al. Granger causal inference based on dual laplacian distribution and its application to MI-BCI classification
Naser et al. sEMG-Based hand gestures classification using a semi-supervised multi-layer neural networks with Autoencoder
CN110321856A (en) A kind of brain-machine interface method and device of the multiple dimensioned divergence CSP of time-frequency
Li et al. Deep end-to-end transfer learning for robust inter-subject and inter-day hand gesture recognition using surface EMG
Lu Human emotion recognition based on multi-channel EEG signals using LSTM neural network
Zhou et al. MI-EEG temporal information learning based on one-dimensional convolutional neural network
Kumar et al. Advances in non-invasive EEG-based brain-computer interfaces: Signal acquisition, processing, emerging approaches, and applications
Shoryabi et al. A Novel Neural Approach For Classification of EEG Signals for Brain-Computer Interface