A study of action difference on motor imagery based on delayed matching posture task.

Objective. Motor imagery (MI)-based brain-computer interfaces (BCIs) provide an additional control pathway for people by decoding the intention of action imagination. The way people imagine greatly affects MI-BCI performance. Action itself is one of the factors that influence the way people imagine. Whether the different actions cause a difference in the MI performance is unknown. What is more important is how to manifest this action difference in the process of imagery, which has the potential to guide people to use their individualized actions to imagine more effectively.Approach.To explore action differences, this study proposes a novel paradigm named as action observation based delayed matching posture task. Ten subjects are required to observe, memorize, match, and imagine three types of actions (cutting, grasping and writing) given by visual images or videos, to accomplish the phases of encoding, retrieval and reinforcement of MI. Event-related potential (ERP), MI features, and classification accuracy of the left or the right hand are used to evaluate the effect of the action difference on the MI difference.Main results.Action differences cause different feature distributions, resulting in that the accuracy with high event-related (de)synchronization (ERD/ERS) is 27.75% higher than the ones with low ERD/ERS (p< 0.05), which indicates that the action difference has impact on the MI difference and the BCI performance. In addition, significant differences in the ERP amplitudes exists among the three actions: the amplitude of P300-N200 potential reaches 9.28µV of grasping, 5.64µV and 5.25µV higher than the cutting and the writing, respectively (p< 0.05).Significance.The ERP amplitudes derived from the supplementary motor area shows positive correlation to the MI classification accuracy, implying that the ERP might be an index of the MI performance when the people is faced with action selection. This study demonstrates that the MI difference is related to the action difference, and can be manifested by the ERP, which is important for improving MI training by selecting suitable action; the relationship between the ERP and the MI provides a novel index to find the suitable action to set up an individualized BCI and improve the performance further.

Effects of Motor Imagery Tasks on Brain Functional Networks Based on EEG Mu/Beta Rhythm.

Motor imagery (MI) refers to the mental rehearsal of movement in the absence of overt motor action, which can activate or inhibit cortical excitability. EEG mu/beta oscillations recorded over the human motor cortex have been shown to be consistently suppressed during both the imagination and performance of movements, although the specific effect on brain function remains to be confirmed. In this study, Granger causality (GC) was used to construct the brain functional network of subjects during motor imagery and resting state based on EEG in order to explore the effects of motor imagery on brain function. Parameters of the brain functional network were compared and analyzed, including degree, clustering coefficient, characteristic path length and global efficiency of EEG mu/beta rhythm in different states. The results showed that the clustering coefficient and efficiency of EEG mu/beta rhythm decreased significantly during motor imagery (p < 0.05), while degree distribution and characteristic path length increased significantly (p < 0.05), mainly concentrated in the frontal lobe and sensorimotor area. For the resting state after motor imagery, the changes of brain functional characteristics were roughly similar to those of the task state. Therefore, it is concluded that motor imagery plays an important role in activation of cortical excitability.

A TrAdaBoost Method for Detecting Multiple Subjects' N200 and P300 Potentials Based on Cross-Validation and an Adaptive Threshold.

Traditional training methods need to collect a large amount of data for every subject to train a subject-specific classifier, which causes subjects fatigue and training burden. This study proposes a novel training method, TrAdaBoost based on cross-validation and an adaptive threshold (CV-T-TAB), to reduce the amount of data required for training by selecting and combining multiple subjects' classifiers that perform well on a new subject to train a classifier. This method adopts cross-validation to extend the amount of the new subject's training data and sets an adaptive threshold to select the optimal combination of the classifiers. Twenty-five subjects participated in the N200- and P300-based brain-computer interface. The study compares CV-T-TAB to five traditional training methods by testing them on the training of a support vector machine. The accuracy, information transfer rate, area under the curve, recall and precision are used to evaluate the performances under nine conditions with different amounts of data. CV-T-TAB outperforms the other methods and retains a high accuracy even when the amount of data is reduced to one-third of the original amount. The results imply that CV-T-TAB is effective in improving the performance of a subject-specific classifier with a small amount of data by adopting multiple subjects' classifiers, which reduces the training cost.

The ratio of H/C is a useful parameter to predict adsorption of the herbicide metolachlor to biochars.

Biochar adsorbent was produced by pyrolyzing traditional Chinese medicinal herb residue at 300, 500 and 750 °C (referred to as biochar-300, biochar-500 and biochar-750). Basic physical and chemical analyses, Fourier transform infrared spectroscopy (FT-IR), and thermodynamic analyses were performed to elucidate adsorption and properties of biochar. Biochar adsorption capacity of herbicide metolochlor, as measured by batch-type adsorption experiments by Freundlich constant Kf (mg1-n Ln kg-1), followed the order: biochar-750 > biochar-300 > biochar-500. Thermodynamic analysis suggested that adsorption of metolachlor on biochar was a spontaneous process. The adsorption isotherm for the biochar produced at the highest pyrolysis temperature was characteristic for adsorption process driven by a high surface area of biochar (85.30 m2 g-1), while the adsorption process for the biochar produced at the lowest temperature was controlled by its higher content of organic matter (39.06%) and abundant functional groups. The FT-IR spectra also showed that the biochar prepared at the lowest temperature had the highest number of surface groups. In general, pore-filling induced by the large surface area of the biochar was the dominant adsorption mechanism. When the H/C value was >0.5, the adsorption mechanism of biochar was dominated by surface chemical bond, while pore-filling played a major role when the H/C value was <0.5.

Cerebral cortex functional networks of magnetic stimulation at acupoints along the pericardium meridian.

The coordinated regulating mechanism of magnetic stimulation at acupoints along the pericardium meridian was studied by cerebral cortical functional networks. Electroencephalogram signals of 14 subjects (eight males and six females) were recorded in resting state and following magnetic stimulation at Neiguan and Daling acupoints along the pericardium meridian. The corresponding cortical functional networks were constructed and analyzed by group independent component analysis, standard low-resolution brain electromagnetic tomography, short-time Fourier transform, and complex network theory. Results showed that during magnetic stimulation at Daling and Neiguan acupoints, the functional connections of the nodes in the brain areas associated with movement respectively decreased by 7.3% and 19.9%, and the functional connections of the nodes in brain areas associated with advanced cognitive functions such as emotion, memory and language respectively increased by 24.9% and 18.8%. Changes of topological structure were similarly related to the efficacy of acupoints along the pericardium meridian. Magnetic stimulation also caused different topological changes consistent with the therapeutic function of specific acupoint. This study provided new evidence revealing mechanisms of brain functional integration and network synergy in the acupoint stimulation pathway.

[Research on the methods for electroencephalogram feature extraction based on blind source separation].

In the present investigation, we studied four methods of blind source separation/independent component analysis (BSS/ICA), AMUSE, SOBI, JADE, and FastICA. We did the feature extraction of electroencephalogram (EEG) signals of brain computer interface (BCI) for classifying spontaneous mental activities, which contained four mental tasks including imagination of left hand, right hand, foot and tongue movement. Different methods of extract physiological components were studied and achieved good performance. Then, three combined methods of SOBI and FastICA for extraction of EEG features of motor imagery were proposed. The results showed that combining of SOBI and ICA could not only reduce various artifacts and noise but also localize useful source and improve accuracy of BCI. It would improve further study of physiological mechanisms of motor imagery.

Somatosensory-evoked potentials and cortical activities evoked by magnetic stimulation on acupoint in human.

Two acupuncture manipulations are clinically used: manual manipulation and electrical acupuncture. There is little published on the EEG changes during magnetic stimulation on an acupuncture site. In this study, EEG data in response to magnetic stimulation on HeGu (LI 4) acupoint were measured to determine whether magnetic acupoint stimulation might modulate ongoing EEG or not. Eighteen healthy volunteers (13 male, 5 female) 20 to 35 years old were chosen in this experiment, with consent obtained before the study. The highest evoked potential was recorded in FCZ electrode, at about 140-170 ms (P150) after acupoint stimulation, but not mock point stimulation. Comparison of the somatosensory-evoked potentials in response to acupoint stimulation and mock point stimulation showed that P150 was specific to acupoint stimulation. With regard to the location of P150 in the human brain, we suggest that magnetic stimulation on HeGu acupoint would affect specific brain areas compared with the mock point. The difference in the anatomical structure of acupoint and non-acupoint may explain the specific acupoint-brain correlation, and P150 may be a characteristic activation in response to acupoint afferent.

Feature extraction of mental task in BCI based on the method of approximate entropy.

Brain computer interface (BCI) is based on processing brain signals recorded from the scalp or the surface of the cortex in order to identify the different brain states and covert to corresponded control command. The key problems in BCI research are feature extraction and classification. In this paper, two experiments were performed, and the EEG data were recording during each experiment. One experiment contains five mental tasks, including "baseline", "rotation", "multiplication", "counting" and "letter-composing", the other contains two mental tasks which are left hand imagery movement and right hand imagery movement. EEG data recorded from both experiments are analyzed by approximate entropy (Apen), which is used to extract the characteristic feature of different mental tasks. A three-layer BP Neural Network classifier was structured for pattern classification. Different results were gained from the mental task experiment and imagery movement experiment. The results show that Apen is an effective method to extract the feature of different brain states.