Baolei Xu

Baolei Xu
Huawei Technologies · Department of R&D

PhD

About

26
Publications
21,582
Reads
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228
Citations
Additional affiliations
August 2008 - present
Chinese Academy of Sciences
Position
  • PhD Student
August 2008 - July 2014
Chinese Academy of Sciences
Position
  • PhD Student
Education
August 2010 - December 2013
Independent Researcher
Field of study
  • Brain Computer Interface

Publications

Publications (26)
Article
Multi-modal brain-computer interface and multi-modal brain function imaging are developing trends for the present and future. Aiming at multi-modal brain-computer interface based on electroencephalogram-near infrared spectroscopy (EEG-NIRS) and in order to simultaneously acquire the brain activity of motor area, an acquisition helmet by NIRS combin...
Article
Simultaneous acquisition of brain activity signals from the sensorimotor area using NIRS combined with EEG, imagined hand clenching force and speed modulation of brain activity, as well as 6-class classification of these imagined motor parameters by NIRS-EEG were explored. Near infrared probes were aligned with C3 and C4, and EEG electrodes were pl...
Article
Full-text available
Functional near-infrared spectroscopy (fNIRS) is an emerging optical technique, which can assess brain activities associated with tasks. In this study, six participants were asked to perform three imageries of hand clenching associated with force and speed, respectively. Joint mutual information (JMI) criterion was used to extract the optimal featu...
Article
Full-text available
In order to increase the number of states classified by a brain-computer interface (BCI), we utilized a motor imagery task where subjects imagined both force and speed of hand clenching. The BCI utilized simultaneously recorded electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The time-phase-frequency feature...
Article
Near-infrared spectroscopy (NIRS) is a non-invasive optical technique used for brain–computer interface (BCI). This study aims to investigate the brain hemodynamic responses of clench force and speed motor imagery and extract task-relevant features to obtain better classification performance. Given the non-stationary characteristics of real hemodyn...
Article
Full-text available
The empirical mode decomposition (EMD) has been introduced quite recently to adaptively decompose non-stationary and/or nonlinear time series. Spike wave of the MGG signal is very important and standard diagnose method for medical and clinical research. While traditional analysis method may not calculate the stable the result. In this paper, we int...
Article
Full-text available
We introduce a new motor parameter imagery paradigm using clench speed and clench force motor imagery. The time-frequency-phase features are extracted from mu rhythm and beta rhythms, and the features are optimized using three process methods: no-scaled feature using "MIFS" feature selection criterion, scaled feature using "MIFS" feature selection...
Article
Full-text available
Time–domain feature representation for imagined grip force movement-related cortical potentials (MRCP) of the right or left hand and the decoding of imagined grip force parameters based on electroencephalogram (EEG) activity recorded during a single trial were here investigated. EEG signals were acquired from eleven healthy subjects during four dif...
Article
Full-text available
In this paper, we investigate the phase information for classification between clench speed and clench force motor imagery for BCI applications. The multivariate extensions of empirical mode decomposition (MEMD) are used to decompose EEG data into intrinsic mode functions (IMFs). Then, the phase information is got by transforming IMFs into analytic...
Article
Full-text available
In this paper, we present a signal discretization and feature selection method to improve classification accuracy for fNIRS based brain computer interface (BCI) system, which can classifiy right hand clench force motor imagery and clench speed motor imagery at an accuracy of 69%-81% through 5 fold cross validation in 6 subjects. Difference between...
Conference Paper
Full-text available
In this paper, we apply a new method to access gastric slow wave through magnetic approach, which can be used to validate gastric motility. The magnetogastrogram (MGG) signals are acquired by two Magnetoimpedance (GMI) sensors at two locations on the skin beside the stomach simultaneously at a sampling frequency of 1000Hz. GMI sensor is reinvented...
Conference Paper
Functional near infrared spectroscopy (fNERS) is a non-invasive technology that can be applied in brain-computer interface (BCI). fNIRS uses light in the range of near infrared to measure activity of the cerebral cortex. Prefrontal cortex is activated when performing mental arithmetic tasks. We captured frontal hemodynamic responses during mental a...
Article
Full-text available
Brain-Computer Interface (BCI) is very useful for people who lose limb control such as amyotrophic lateral sclerosis (ALS) patients, stroke patients and patients with prosthetic limbs. Among all the brain signal acquisition devices, functional near-infrared spectroscopy (fNIRS) is an efficient approach to detect hemodynamic responses correlated wit...
Article
Full-text available
Giant Magnetoimpedance (GMI) sensor is the hot research topics in sensor engineering. In this paper we setup a double channel acquisition system for MGG signal. Then FastICA algorithm is applied in the raw data and We get the spectrum after ICA processing. The results show that ICA is powerful tool for MGG signal processing to separate the noise si...
Article
Full-text available
Lock-in amplifier is particularly important in the fNIRS-based system, because the lock-in amplifier can recover the low-level signals buried in significant amounts of noise. But the price of lock-in amplifier is very expensive. This paper presented a software method for designing digital lock-in amplifier. Compared with analogue lock-in amplifier,...
Article
Full-text available
Direct brain-controlled robot interface (BCRI) is a new type human-robot interface which is an important research and development direction for brain-machine interface (BMI)/brain-computer interface (BCI) in the robot control field. Many experimental researches and developments for BCRI were reported by Nature, Science and other important internati...
Article
Full-text available
Time domain features of slow potentials ≤2 Hz) during periodic movement and motor imagery at fast and slow (4 Hz and 2 Hz) were investigated in 4 healthy subjects by event-related potentials in the paper. EEG was recorded from 64 electrodes and 9 closely spaced electrodes overlaying the left and right sensorimotor area were analyzed. The subjects p...
Conference Paper
Full-text available
In this paper, we present a method for classifying functional near-infrared spectroscopy (fNIRS) data using wavelets and support vector machine (SVM). fNIRS data is acquired by ETG-4000 during speed and force imagination. Probes location is around C3 and C4 in 10-20 international system. After preprocessing the data using NIRS-SPM, we decompose it...
Article
Full-text available
Reactive rhythm bands to imagined speeds of index finger movement and offline classification of imagined speeds were explored in the paper. EEG was recorded when 4 subjects executed motor imagery of two tasks at first-person perspective that involved left index fingers at two speeds (4Hz and 1Hz, trained and paced by metronome). Relatively prominen...
Article
Full-text available
Phase-locked and non-phase-locked event-related oscillations and channel spectra during motor imagery with speed parameters (fast 4Hz and slow 1Hz) were investigated in the paper. EEG signals related to imagination of six tasks that involved three limbs (left and right index fingers and right toes) and two speeds were first separated from original...
Conference Paper
Full-text available
The study explored event-related perturbation in spectral power and in potentials during periodic fast and slow motor imagination of left and right index finger and right toe based on EEG. EEG signals were collected from 4 healthy volunteers during imagination of six tasks that involved three limbs (left and right index fingers and right toes ) and...
Conference Paper
Full-text available
In this paper, we present a Brain Computer Interface (BCI) system using multichannel functional near-infrared spectroscopy (fNIRS) signal acquired when subjects execute speed and force imagination of right hand. Our goal is to classify much more movement imagination details so that a BCI system can provide more control commands, which is helpful f...
Article
Full-text available
In the study of Internet-based telerobot system, the aim is at human-machine integration and a BMI-based telerobot system over Internet is proposed using a new method of direct human-machine integration interface: brain-machine interface (BMI). The difference between this system and the traditional Internet-based telerobot system lies in that opera...

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