Khalaf et al., 2019 - Google Patents
EEG-fTCD hybrid brain–computer interface using template matching and wavelet decompositionKhalaf et al., 2019
View PDF- Document ID
- 5844211770811258597
- Author
- Khalaf A
- Sejdic E
- Akcakaya M
- Publication year
- Publication venue
- Journal of Neural Engineering
External Links
Snippet
Objective. We aim at developing a hybrid brain–computer interface that utilizes electroencephalography (EEG) and functional transcranial Doppler (fTCD). In this hybrid BCI, EEG and fTCD are used simultaneously to measure electrical brain activity and …
- 238000000354 decomposition reaction 0 title abstract description 13
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
- 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
- A61B5/04—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
- A61B5/72—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
- A61B5/40—Detecting, measuring or recording for evaluating the nervous system
- A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
-
- 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
-
- 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
-
- 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
- A61B5/05—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
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Gorjan et al. | Removal of movement-induced EEG artifacts: current state of the art and guidelines | |
Garipelli et al. | Single trial analysis of slow cortical potentials: a study on anticipation related potentials | |
Arpaia et al. | How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art | |
Mondini et al. | Continuous low-frequency EEG decoding of arm movement for closed-loop, natural control of a robotic arm | |
Liao et al. | Decoding individual finger movements from one hand using human EEG signals | |
Korik et al. | Decoding imagined 3D hand movement trajectories from EEG: evidence to support the use of mu, beta, and low gamma oscillations | |
Daly et al. | Affective brain–computer music interfacing | |
Siuly et al. | Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain–computer interface | |
Guarnieri et al. | Online EEG artifact removal for BCI applications by adaptive spatial filtering | |
Nazeer et al. | Enhancing classification accuracy of fNIRS-BCI using features acquired from vector-based phase analysis | |
Fang et al. | Extracting features from phase space of EEG signals in brain–computer interfaces | |
Paek et al. | Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography | |
Xiao et al. | Evaluation of EEG features in decoding individual finger movements from one hand | |
Jiang et al. | Characterization and decoding the spatial patterns of hand extension/flexion using high-density ECoG | |
Shamsi et al. | Early classification of motor tasks using dynamic functional connectivity graphs from EEG | |
Xiao et al. | EEG resolutions in detecting and decoding finger movements from spectral analysis | |
Breitwieser et al. | A hybrid three-class brain–computer interface system utilizing SSSEPs and transient ERPs | |
Hsu et al. | Monitoring alert and drowsy states by modeling EEG source nonstationarity | |
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 | |
Catrambone et al. | Toward brain–heart computer interfaces: A study on the classification of upper limb movements using multisystem directional estimates | |
Ko et al. | SSVEP-assisted RSVP brain–computer interface paradigm for multi-target classification | |
Hosseini et al. | Continuous decoding of hand movement from EEG signals using phase-based connectivity features | |
Kumar et al. | Classification of error-related potentials evoked during stroke rehabilitation training | |
Iwane et al. | Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states |