0000000000125117

AUTHOR

X. Longfei

showing 2 related works from this author

Detection of steering direction using EEG recordings based on sample entropy and time-frequency analysis.

2016

Monitoring driver's intentions beforehand is an ambitious aim, which will bring a huge impact on the society by preventing traffic accidents. Hence, in this preliminary study we recorded high resolution electroencephalography (EEG) from 5 subjects while driving a car under real conditions along with an accelerometer which detects the onset of steering. Two sensor-level analyses, sample entropy and time-frequency analysis, have been implemented to observe the dynamics before the onset of steering. Thus, in order to classify the steering direction we applied a machine learning algorithm consisting of: dimensionality reduction and classification using principal-component-analysis (PCA) and sup…

Automobile DrivingSupport Vector MachineComputer scienceSpeech recognitionEntropyElectroencephalography03 medical and health sciencesEntropy (classical thermodynamics)0302 clinical medicine0502 economics and businessAccelerometrymedicineEntropy (information theory)HumansEntropy (energy dispersal)Entropy (arrow of time)050210 logistics & transportationPrincipal Component Analysismedicine.diagnostic_testbusiness.industryEntropy (statistical thermodynamics)Dimensionality reduction05 social sciencesPattern recognitionElectroencephalographyTime–frequency analysisSupport vector machineSample entropyPrincipal component analysisArtificial intelligencebusiness030217 neurology & neurosurgeryAlgorithmsEntropy (order and disorder)Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
researchProduct

Establishing and validating a new source analysis method using phase.

2017

Electroencephalogram (EEG) measures the brain oscillatory activity non-invasively. The localization of deep brain generators of the electric fields is essential for understanding neuronal function in healthy humans and for damasking specific regions that cause abnormal activity in patients with neurological disorders. The aim of this study was to test whether the phase estimation from scalp data can be reliably used to identify the number of dipoles in source analyses. The steps performed included: i) modeling different phasic oscillatory signals using auto-regressive processes at a particular frequency, ii) simulation of two different noises, namely white and colored noise, having differen…

0301 basic medicinePhase (waves)ElectroencephalographySignal-To-Noise RatioTemporal lobe03 medical and health sciencesEpilepsy0302 clinical medicineSignal-to-noise ratiomedicineHumansAnalysis methodBrain Mappingmedicine.diagnostic_testbusiness.industryBrainPattern recognitionElectroencephalographymedicine.disease030104 developmental biologymedicine.anatomical_structureEpilepsy Temporal LobeColors of noiseScalpArtificial intelligencePsychologybusinessNeuroscience030217 neurology & neurosurgeryAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
researchProduct