Brain-computer interface provides a new communication bridge between the human mind and devices, depending largely on accurate classification identification of non-invasive EEG signals. Recently, deep learning approaches have been widely used in many fields to extract features classify various types data successfully. However, approach requires massive train its neural networks, amount impacts greatly quality classifiers. This paper proposes novel that combines augmentation for...

Surface electromyogram (sEMG) signals can be applied in medical, rehabilitation, robotic, and industrial fields. As a typical application, myoelectric prosthetic hand is controlled by the sEMG of amputee's residual muscles. To improve dexterity hand, additional motion commands need to classified. The more sensors are used, However, muscles limited. In order practicability it critical investigate effective pattern recognition algorithms deal with detected fewer sensors, while identifying as...

Abstract Objective . Alzheimer’s disease is a progressive neurodegenerative dementia that poses significant global health threat. It imperative and essential to detect patients in the mild cognitive impairment (MCI) stage or even earlier, enabling effective interventions prevent further deterioration of dementia. This study focuses on early prediction utilizing Magnetic Resonance Imaging (MRI) data, using proposed Graph Convolutional Networks (GCNs). Approach Specifically, we developed...

The challenge of this work is to study the design and development human-robot collaboration (HRC) in cellular manufacturing. Based on concept human collaborative design, four main factors are being identified developed an active HRC prototype production cell for cable harness assembly. Human aims optimize system advantage between robots based considerations. Task modeling approach analyze task order identify tasks develop planning. In safety development, five designs, cover both hardware...

Current EEG-based brain-computer interface technologies mainly focus on how to independently use SSVEP, motor imagery, P300, or other signals recognize human intention and generate several control commands. SSVEP P300 require external stimulus, while imagery does not it. However, the generated commands of these methods are limited cannot a robot provide satisfactory service user. Taking advantage both this paper aims design hybrid BCI system that can multimodal robot. In system, three used...

Previous studies made progress in the early diagnosis of Alzheimer's disease (AD) using electroencephalography (EEG) without considering EEG connectivity. To fill this gap, we explored significant differences between AD patients and controls based on frequency domain spatial properties functional connectivity mild cognitive impairment (MCI) datasets. Four global metrics, network resilience, connection-level metrics node versatility were used to distinguish patients. The results show that...

Most myoelectric prosthetic hands use a fixed pattern recognition model to identify the user's hand motion commands. Since surface electromyogram (sEMG) characteristics vary with time, it is difficult employ in identifying commands stably for long period of time. In order adapt gradual changes sEMG characteristics, we utilized incremental learning based on wavelet neural network (WNN) ensemble, and used negative correlation (NCL) train it. To verify effect proposed method, group subjects...

Planes and edges are attractive features for simultaneous localization mapping (SLAM) in indoor environments because they can be reliably extracted robust to illumination changes. However, it remains a challenging problem seamlessly fuse two different kinds of avoid degeneracy accurately estimate the camera motion. In this article, plane-edge-SLAM system using an RGB-D sensor is developed address seamless fusion planes edges. Constraint analysis first performed obtain quantitative measure...

Brain-computer interface (BCI) technologies have been widely used in many areas. In particular, non-invasive such as electroencephalography (EEG) or near-infrared spectroscopy (NIRS) to detect motor imagery, disease, mental state. It has already shown literature that the hybrid of EEG and NIRS better results than their respective individual signals. The fusion algorithm for sources is key implement them real-life applications. this research, we propose three methods NIRS-based brain-computer...

Surface electromyogram (sEMG) signals have been used to control multifunctional prosthetic hands. Researchers usually focused on the use of several channels with sEMG identify more gestures without limiting number sensors. However, residual muscles an amputee are limited. Therefore, point a successful recognition system is decrease classify gestures. To achieve this goal, we proposed novel gesture system, in which three can nine In time domain features, root mean square ratio, and...

Treadmills are widely used to recover walking function in the rehabilitation field for those patients with gait disorders. Nevertheless, ultimate goal of recovery is walk on ground rather than treadmill. This study aims determine effect treadmill and upper trunk movement characteristics using wearable sensors. Eight healthy male subjects recruited perform 420-m straight overground (OW) 5 min (TW), wearing 3 inertial measurement units a pair insole In addition common linear features,...

Welding defect constitute a great danger to the safe usage of petroleum pipelines. In addition, manual welding detection has numerous deficiencies, such as subjectivity and inaccurate estimation geometric parameters. Thus, we proposed an automatic system for X-ray images. system, five typical defects (cracks, lack penetration, fusion, round defects, stripy defects) non-defects were chosen recognition. There are three stages in system: extraction, detection, first stage, background...

This paper presents a new brain-robot interaction system by fusing human and machine intelligence to improve the real-time control performance. consists of hybrid P300 steady-state visual evoked potential (SSVEP) mode conveying being's intention, combining fuzzy-logic-based image processing algorithm with multi-sensor fusion technology. A subject selects an object interest via P300, classification transfers corresponding parameters improved fuzzy color extractor for extraction. central...

Automatic driving vehicles have been developed to provide more convenient and comfortable experiences. However, these failed in satisfying the variance of human intentions. Recently, strategy collaborating brain–computer interface (BCI) controlling automatic receives attention. Since BCI system remained some limitation real-time controlling, a fusion method has proposed explore verify feasibility human-vehicle collaborative this paper. A hybrid was interpret In addition, computer...

Our research aims to design and develop a safety strategy for human–robot collaboration system. Although robotic assistance in cellular manufacturing system is promising, the uppermost consideration before it can be materialized. Five main designs are developed this work. (i) Safe working areas humans robots. (ii) To control behavior of robot based on requirements, light curtains defined safe collaborative zones. (iii) Additionally, was using mechanical Dual Check Safety strategies terms...

The hand plays a very important role in our daily life, and the amputees suffer lot from loss of hands or upper limbs. Hence, assisting devices are desired urgently. Today, prosthetic based on surface electromyography (sEMG) signals can recognize many gestures, but some problems still exist. To identify more recognition systems require multiple electrodes, which unable to be applied with less residual muscles. Meanwhile, better computing performance is required as number electrodes...

Empirical mode decomposition (EMD) has developed into a prominent tool for adaptive, scale-based signal analysis in various fields like robotics, security and biomedical engineering. Since the dramatic increase amount of data puts forward higher requirements capability real-time analysis, it is difficult existing EMD its variants to trade off growth dimension speed analysis. In order decompose multi-dimensional signals at faster speed, we present novel signal-serialization method...

The convolutional neural network (CNN) has emerged as a powerful tool for decoding electroencephalogram (EEG), which owns the potential use in event-related potential-based brain-computer interface (ERP-BCI). However, intra-individual difference of ERP makes traditional learning models trained on static EEG data hard to decode when features vary along time, limits long-time performance model. Addressing this problem, study proposes three-dimension CNN (3D-CNN)-based model ERPs dynamically....

Objectives The pathological mechanism for a disorder of consciousness (DoC) is still not fully understood. Based on traditional behavioral scales, there high rate misdiagnosis subtypes DoC. We aimed to explore whether topological characterization may explain the mechanisms DoC and be effective in diagnosing Methods Using resting-state functional magnetic resonance imaging data, weighted brain networks normal control subjects patients with vegetative state (VS) minimally conscious (MCS) were...