The multi-locomotion robot (MLR), including bionic insect microrobot, animal and so on, should choose different locomotion modes according to the obstacles it faces. However, under modes, power consumption, moving speed, falling risk of MLR are different, in most cases, they mutually exclusive. This paper proposes a path planning algorithm for based on multi-objective genetic with elitist strategy (MLRMOEGA), which has four optimization objectives: time risk, smoothness. We propose two...

Functional brain network (FBN) has become very popular to analyze the interaction between cortical regions in last decade. But researchers always spend a long time search best way compute FBN for their specific studies. The purpose of this study is detect proficiency operators during mineral grinding process controlling based on FBN. To save time, novel semi-data-driven method computing functional connection stacked autoencoder (BCSAE) proposed paper. This uses (SAE) encode multi-channel EEG...

Traditional manual concrete vibration work faces numerous limitations, necessitating the need for efficient automated methods to support this task. This study proposes a path safety optimization method based on safe flight corridors and Euclidean signed distance fields, which is suitable flexible autonomous movement of vibrating robots between various points. By utilizing vector generate optimizing with proposed reduces runtime by 80% compared original corridor enhances 50%. On embedded...

Although resting-state functional magnetic resonance imaging has shown altered connectivity between visual and other brain areas in the early blind individuals, it cannot answer which area's local activities are changed. In this study, regional homogeneity, a measure of homogeneity blood oxygen level-dependent signals, was used for first time to investigate changes activity individuals. Compared with age-matched sex-matched sighted individuals showed increased only occipital areas, might be...

We analyzed the frequency of heart rate (HR) changes related to seizures, and we sought identify influencing factors these during partial summarize regularity HR gain some insight into mechanisms involved in neuronal regulation cardiovascular function. To date, detailed information on seizures by multiple linear regression analysis remains scarce. Using video-electroencephalograph (EEG)-electrocardiograph (ECG) recordings, retrospectively assessed 81 patients a total 181 including 27 simple...

This paper describes the method for classifying multiclass motor imagery EEG signals of brain-computer interfaces (BCIs) according to phenomena event-related desynchronization and synchronization (ERD/ERS). The one-versus-one common spatial pattern (CSP) feature extraction was employed. And we extended two different kinds classifiers: 1) support vector machines (SVM) based on maximal average decision value; 2) k-nearest neighbor (KNN) rule classification. In order testify performance each...

This paper mainly studies about the data processing of brain computer interface(BCI) and presents a kind method for classifying ECoG motor imagery tasks. Both training testing datasets were filtered with frequency band 8-30Hz according to event-related desynchronization synchronization(ERD/ERS) phenomenon. The features extracted by using Common Spatial Pattern(CSP) then best starting ending points decided through ten-fold cross validation(CV) dataset. All fed into linear support vector...

Electrocorticography (ECoG) signals have been proved to be associated with different types of motor imagery and used in brain-computer interface (BCI) research. This paper studies the channel selection feature extraction using band powers (BP) for a typical ECoG-based BCI system. The subject images movement left finger or tongue. Firstly, BP features were selection, 11 channels which had distinctive selected from 64 channels. Then, ECoG extracted BP, dimension vector was reduced principal...

In this study, a brain-computer interface (BCI) using electrocorticograms (ECoG) is proposed. Feature extraction an important task that significantly affects the classification results. First, discrete wavelet transform was applied to ECoG signals from one subject performing imagined movements of either left small-finger or tongue. After preprocessing, relative energy selected 8 channels were extracted and built 40 dimension feature vector. Then vector reduced principal component analysis...

Objective: The objective of this study was to use functional connectivity and graphic indicators investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) effect acute deep stimulation (DBS) on those in patients with PD. Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) 20 PD who were DBS-OFF state DBS-ON during resting eyes closed. A source method used identify networks. Power spectral density (PSD) analysis...

Electrocorticograms (ECoG) signals have many potential advantages and gained much attention for use with brain-computer interface (BCI). In this study, feature extraction using band powers was applied to ECoG from one subject performing imagined movements of either the left small-finger or tongue. Probabilistic neural network (PNN) which very suitable classification problems used classify two different imaginary movements. The accuracy rate test data set reached a maximum 86% when spread...

Deception is a human behavior and its cognitive process mechanism involve complex neuronal activities of the brain. In this article, we develop simple feasible concealed information test (CIT) method which based on audio-visual event-related potentials (ERPs) spatial temporal features. The main purpose article to extend pattern recognition with functional network parameters global feature entropy EEG signals from whole At same time, novel quantum neural (QNN) classifier was developed...

For the BCI research to classify different imagined movements of both left and right hands, a method using wavelet packet decomposition for feature extraction SVM pattern classification was adopted. Firstly discusses transform in depth brings out an idea taking coefficients' variance as into account, then extracts serials after channel C3 C4, finally, patterns by linear SVM. The result shows that maximum accuracy is 86.43% suitable. So, this paper used more efficient simpler, it gives new...

For a typical electrocorticogram(ECoG)-based brain-computer interface(BCI) system, pattern recognition algorithm using wavelet analysis and Fisher linear discriminant analysis(FLDA) was proposed. Firstly, based on studying theory, novel feature extraction method in ECoG signal processing namely variance(WV) or packet variance(WPV) proposed considering the band interlacing phenomenon transform, computing of WV/WPV brought out; then, taken as feature, WVs WPVs 6 most important channels were...

Brain-computer interface (BCI) uses brain activity for communication and control of objects in their environment without the participation peripheral nerves muscles. BCI technology can help improve quality life restore functions people with severe motor disabilities. We used combinations wavelet entropy (WE) band powers (BP) feature extraction system which was based on imaginary left right hand movements. Linear discriminant analysis (LDA) classification mutual information (MI) evaluation...

The electrocorticogram(ECoG) is proved to have high signal-to-noise ratio(SNR), which makes it better fitting for BCIs. And this paper represents a kind of classification method ECoG signals motor imagery tasks(left finger and tongue). Band power(BP) with the frequency band [8 30] was extracted as feature, linear discriminant analysis(LDA), k-nearest neighbor(kNN) rules support vector machine(SVM) were used classifiers. From results these three classifiers, kNN k=7 performed than all other...