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As a promising non-invasive technique, functional near-infrared spectroscopy(fNIRS) can easily detect the hemodynamic responses of cortical brain activities. This paper investigated the multiclass classification of motor imagery(MI)based on fNIRS. 10 healthy individuals were recruited to move an object using their imagination. A multi-channel continuous-wave fNIRS equipment was applied to obtain the...
Depression is a mental disorder of high prevalence, leading to a negative effect on individuals, their families, society and the economy. In recent years, the problem of automatic detection of depression from the speech signal has gained more interest. In this paper, a new multiple classifier system for depression recognition was developed and tested. The novel aspect of this methodology is the combination...
Emotion recognition using EEG signals has become a hot research topic in the last few years. This paper aims at providing a novel method for emotion recognition using less channels of frontal EEG signals. By employing the asymmetry theory of frontal brain, a new method fusing spatial and frequency features was presented, which only adopted two channels of frontal EEG signals at Fp1 and Fp2. In order...
As the ∊4 allele of apolipoprotein E (APOE4) is proved a high risk factor of Alzheimer's disease (AD), numerous studies have used modalities of neuroimaging data to investigate the alterations of brain caused by APOE4. A recent study has shown that APOE4-related pathological changes of cortical networks during rest exist in APOE4 carriers. However, the interrelationship among the resting intrinsic...
Recently, Electroencephalogram (EEG) has become increasingly important in the role of psychiatric diagnosis and emotion recognition. However, many irrelevant features make it difficult to identify patterns accurately. Obtaining valid features from electroencephalogram can improve the classification and generalization performance. In this paper, an improved normalized mutual information feature selection...
We propose a joint intrinsic-extrinsic prior model to estimate both illumination and reflectance from an observed image. The 2D image formed from 3D object in the scene is affected by the intrinsic properties (shape and texture) and the extrinsic property (illumination). Based on a novel structure-preserving measure called local variation deviation, a joint intrinsic-extrinsic prior model is proposed...
Along with more and more people and cars appear in cities, the stricter requirements, like traffic efficiency, safety, service and cleanness cannot be met easily by using the existing urban traffic plan and operation management methods, which are normally supported by the separated and partial Intelligent Transportation System (ITS). In this paper, big data platform for urban public transportation...
Conventional metallic metasurfaces are difficult to achieve active tuning once a structure is fabricated. We propose a method to realize actively tunable metasurfaces by graphene-metal hybrid structures. Tunable metasurface lenses in mid-infrared and terahertz frequencies are designed.
The development of a three-dimensional (3D) formation controller for unmanned aerial vehicles (UAVs) associated with an event-triggered transmission protocol is presented in this paper. The formation controller is derived based on a 3D relative kinematic model established upon a local-level coordinate frame, which does not require any absolute global information. The proposed controller is developed...
In the process of establishing evaluation index system of physical education, the traditional methods setting weights for each indicator mainly include analytic hierarchy process, fuzzy comprehensive evaluation method, and Delphi method, etc. These methods mostly rely on experience, which is strongly influenced by artificial factors and cannot be avoided. Because artificial neural network model has...
This paper studies a group of interconnected memristor-based impulsive neural networks (MINNs) with time-delay and its synchronization mechanism. Due to the impulsive and switching mode, interconnected MINNs are mathematically elaborated in the form of impulsive differential inclusions. Based on theories of Lyapunov functions and impulsive differential equations, asymptotic convergence of synchronization...
First-order iterative optimization methods play a fundamental role in large scale optimization and machine learning. This paper presents control interpretations for such optimization methods. First, we give loop-shaping interpretations for several existing optimization methods and show that they are composed of basic control elements such as PID and lag compensators. Next, we apply the small gain...
Modern manufacturing systems are human robot systems that consist of human operators and intelligent robots collaborating with each other to accomplish complex tasks. The system performance of such human robot systems relies heavily on reliable and efficient human robot collaborations, which may be seriously compromised due to temporal variations in human to robot trust. This paper proposes to model...
The Automatic Identification System (AIS) is an automatic tracking system which has been widely applied in the fields of intelligent transportation systems, e.g., collision avoidance, navigation, maritime supervision and management. Compare with other positioning systems, e.g., very high frequency (VHF) and radar, AIS can conquer the human errors and it is almost not affected by the external environment...
A major problem of the grid system is identifying the source of abnormal behaviors. It is particularly important to identify the areas in which the grid system suffers from power deficiencies due to fault or voltage instability. Phasor Measurement Units (PMUs) are capable of measuring current and voltage amplitudes and phase angles, which can then be effectively used to monitor the power quality....
Electroencephalogram (EEG) is a noninvasive method to record electrical activity of brain and it has been used extensively in research of brain function due to its high time resolution. However raw EEG is a mixture of signals, which contains noises such as Ocular Artifact (OA) that is irrelevant to the cognitive function of brain. To remove OAs from EEG, many methods have been proposed, such as Independent...
The rapid development of neuroimaging technology and brain network analysis methodologies have promoted the research of Alzheimer's disease (AD). Recently, studies on brain networks reported that AD patients showed abnormal connectivity alterations and disrupted coordinated organizations compared with normal controls (NC). However, much less knowledge is about the abnormalities of metabolic network...
Automatic emotion recognition based on multi-channel neurophysiological signals, as a challenging pattern recognition task, is becoming an important computer-aided method for emotional disorder diagnoses in neurology and psychiatry. Traditional approaches require designing and extracting a range of features from single or multiple channel signals based on extensive domain knowledge. This may be an...
Recent studies suggested that cognitive impairments and memory difficulties in cancer survivors were associated with topology changes of brain network, particularly in terms of the functional and structural abnormalities. However, little is known about the modular reconfiguration of metabolic brain network among this population. In this study, we recruited 78 patients with pre-treatment cancer and...
This paper investigates the potential of physiological signals as reliable channels for multi-subject emotion recognition. A three-stage decision framework is proposed for recognizing four emotions of multiple subjects. The decision framework consists of three stages: (1) in the initial stage, identifying a subject group that a test subject can be mapped to; (2) in the second stage, identifying an...
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