BACKGROUND: Study of imagination offers a perfect setting for study of a large variety of states of consciousness.OBJECTIVE: Here, we studied the characteristics of two electroencephalographic (EEG) patterns evoked by two different imaginary tasks and evaluated the binary classification performance.METHODS: Fifteen individuals (11 male and 4 female, age range of 22 to 33) participated in five sessions of 32-channel EEG recordings.Only by analyzing the subjects' output EEG signals from the central parieto-occipital region of PZ electrode, under the circumstances of consciousness of relaxation-meditation or tension-imagination, we carried out the experiment of feature extraction for spontaneous EEG, as the subjects were blindfolded but asked to open their eyes all the same.The Hilbert-Huang Transform (HHT) was utilized to obtain the Hilbert time-frequency amplitude spectrum, and then with the feature vector set extracted, a two-class Fisher linear discriminant analysis classifier was trained for classification of data epochs of those two tasks.RESULTS: The overall result was that about 90% (± 5%) of the epochs could be correctly classified to their originating task.CONCLUSION: This study not only brings new opportunities for consciousness studies, but also provides a new classification paradigm for achieving control of robots based on the brain-computer interface (BCI).
In robotics and haptics, actuators that move at multiple degrees of freedom (DOFs) without the intermediate transmission mechanisms and have high force/torque output with compact size are widely expected to improve the stability and transparency of interactions. For this reason, utilizing the characteristics that the rheological properties of magnetorheological (MR) fluid can be continuously and reversibly changed by an external magnetic field within a few milliseconds, a multidirectional controlled three-DOF spherical MR actuator is proposed in this paper. Through the special design of the stator part, the actuator can implement force feedback control in multiple directions. Then, based on the calculated torque model and analysis of the magnetic circuit, finite-element analysis is used to optimize the geometry and internal magnetic field distribution of the actuator. In order to achieve precise control and positioning of multi-DOF motion, a small inertial measurement unit is integrated in the upper part of the joystick. We built a prototype of this actuator and tested its performance under various control conditions. The results show that the actuator can provide force feedback with reasonable magnitude and direction to users according to the change of interaction conditions, thus overcoming the disadvantage of the existing spherical MR actuators that limit the movement of the user in all directions after being activated.
In order to improve a mobile robot's autonomy in unknown environments, a novel intelligent controller is designed. The proposed controller is based on fuzzy logic with the aim of assisting a multi-sensor equipped mobile robot to safely navigate in an indoor environment. First, the designs of two behaviors for a robot's autonomous navigation are described, including path tracking and obstacle avoidance, which emulate human driving behaviors and reduce the complexity of the robot's navigation problems in unknown environments. Secondly, the two behaviors are combined by using a finite state machine (FSM), which ensures that the robot can safely track a predefined path in an unknown indoor environment. The inputs to this controller are the readings from the sensors. The corresponding output is the desired direction of the robot. Finally, both the simulation and experimental results verify the effectiveness of the proposed method.
The movement related cortical potential (MRCP) is a well-known neural signature of human's self-paced movement intention, which can be exploited by future neuroprosthesis. Most existing studies have explored the amplitude representation for the movement intention. In this paper we investigate the Hilbert transformed MRCP, which implicitly includes phase information complementarily to amplitude information, for the detection of self-paced upper-limb movement intention. On the datasets in which 5 healthy subjects executed a self-initiated upper limb center-out reaching task in three sessions, we have evaluated the detection model with Hilbert transformed MRCP as features and the state-of-art one with the original MRCP amplitude as features. Results show that the Hilbert transformed MRCP based detector is more accurate than the original MRCP based one.
High force update rate is a key factor for achieving high performance haptic rendering, which imposes a stringent real time requirement upon the execution environment of the haptic system. This requirement confines the haptic system to simplified environment for reducing the computation cost of haptic rendering algorithms. In this paper, we present a novel "hyper-threading" architecture consisting of several threads for haptic rendering. The high force update rate is achieved with relatively large computation time interval for each haptic loop. The proposed method was testified and proved to be effective with experiments on virtual wall prototype haptic system via Delta Haptic Device.
Most machine learning algorithms require a large set of training samples in order to achieve satisfactory performance. However, this requirement may be difficult to satisfy in practice. Take the one-shot learning (OSL) problem on texture recognition for example; the machine learning algorithm is difficult to achieve satisfactory results. In order to solve this problem, a novel multi-modal one-shot learning method for texture recognition is presented. First, in order to improve the robustness of identification and the anti-interference to noise, we addressed the nontravel texture recognition challenges of learn information about object categories from only one training sample by fusing varied modalities data, including image, sound and acceleration, which provides rich information regarding textures. Second, a novel dictionary learning model is designed, which contains the various modalities information, and can simultaneously learn the latent common sparse code for the different modalities. Third, an original regularization term is developed to enhance the degree of distinction of different classes. Furthermore, the common features of the three modalities are evaluated in the case of one-shot learning and used as the basis for feature selection. In the end, experiments were performed based on a data set which was published openly to validate the effectiveness of the presented method.
A novel method based on deformable length of elastic element control (DLEEC) to realize the stiffness display for perception of virtual soft object is proposed, and the stiffness display interface device has been developed and is presented. The stiffness display interface device is composed of a thin elastic beam and an actuator to adjust the length of the beam. The deformation of the beam under a force is proportional to the third power of the beam length. By controlling the beam length, the stiffness display device can reproduce the stiffness of the virtual object from very soft to hard, so that the human fingertip can feel it as if he directly touches with the virtual object by interacting with the device. A real time position control algorithm is employed to guarantee the real time stiffness display.
In this paper, a novel strain-gauge-based three DOF force sensor, which can provide 3 axis force vector for controller by measuring the force real time with wide force measurement range , low coupled interference and relatively low cost, is developed. Firstly, the system structure of the force sensor which contains the sensing element and the sampling circuit with USB interface is introduced. The distribution of strains and the design of Hilton Bridge Circuit have been described in detail. Then, a finite element analysis for the elastic body of the sensor is made. The paper calculates a fixed quantity of static couple under three components with the reasonable finite element computation model, and appraises the rationality of the structural design. Finally, based on the experimental data of the static calibration, the comprehensive and interference errors of the three DOF force sensor are calculated.
