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Human upright postural control is highly related to visual information. In order to investigate the influence of visual feedback on static upright postural control, postural sway of eight healthy adults was investigated under visual feedback circumstances. In the investigation, postural feedback information was visualized by an indicator composed of a(More)
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 two(More)
OBJECTIVE 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. APPROACH The BCI utilized simultaneously recorded electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) signals. The(More)
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(More)
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 features of(More)
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(More)
To study the physiologic mechanism of the brain during different motor imagery (MI) tasks, the authors employed a method of brain-network modeling based on time-frequency cross mutual information obtained from 4-class (left hand, right hand, feet, and tongue) MI tasks recorded as brain-computer interface (BCI) electroencephalography data. The authors(More)
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 operators can(More)
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 placed(More)
For the problem of the EEG signals classification in Brain-computer interfaces (BCI), we proposed a method of feature extraction is that the combination of the wavelet energy feature and phase synchronization feature. In this paper EEG signals are transformed by means of discrete wavelet transform firstly, and then extracted 5 wavelet energy feature in(More)