EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions.

Although target detection based on electroencephalogram (EEG) signals has been extensively investigated recently, EEG-based target detection under weak hidden conditions remains a problem. In this paper, we proposed a rapid serial visual presentation (RSVP) paradigm for target detection corresponding to five levels of weak hidden conditions quantitively based on the RGB color space. Eighteen subjects participated in the experiment, and the neural signatures, including P300 amplitude and latency, were investigated. Detection performance was evaluated under five levels of weak hidden conditions using the linear discrimination analysis and support vector machine classifiers on different channel sets. The experimental results showed that, compared with the benchmark condition, (1) the P300 amplitude significantly decreased (8.92 ± 1.24 µV versus 7.84 ± 1.40 µV, p = 0.021) and latency was significantly prolonged (582.39 ± 25.02 ms versus 643.83 ± 26.16 ms, p = 0.028) only under the weakest hidden condition, and (2) the detection accuracy decreased by less than 2% (75.04 ± 3.24% versus 73.35 ± 3.15%, p = 0.029) with a more than 90% reduction in channel number (62 channels versus 6 channels), determined using the proposed channel selection method under the weakest hidden condition. Our study can provide new insights into target detection under weak hidden conditions based on EEG signals with a rapid serial visual presentation paradigm. In addition, it may expand the application of brain-computer interfaces in EEG-based target detection areas.

A Novel Asynchronous Brain Signals-Based Driver-Vehicle Interface for Brain-Controlled Vehicles.

Directly applying brain signals to operate a mobile manned platform, such as a vehicle, may help people with neuromuscular disorders regain their driving ability. In this paper, we developed a novel electroencephalogram (EEG) signal-based driver-vehicle interface (DVI) for the continuous and asynchronous control of brain-controlled vehicles. The proposed DVI consists of the user interface, the command decoding algorithm, and the control model. The user interface is designed to present the control commands and induce the corresponding brain patterns. The command decoding algorithm is developed to decode the control command. The control model is built to convert the decoded commands to control signals. Offline experimental results show that the developed DVI can generate a motion control command with an accuracy of 83.59% and a detection time of about 2 s, while it has a recognition accuracy of 90.06% in idle states. A real-time brain-controlled simulated vehicle based on the DVI was developed and tested on a U-turn road. Experimental results show the feasibility of the DVI for continuously and asynchronously controlling a vehicle. This work not only advances the research on brain-controlled vehicles but also provides valuable insights into driver-vehicle interfaces, multimodal interaction, and intelligent vehicles.

EEG-Based Adaptive Driver-Vehicle Interface Using Variational Autoencoder and PI-TSVM.

Event-related potential (ERP)-based driver-vehicle interfaces (DVIs) have been developed to provide a communication channel for people with disabilities to drive a vehicle. However, they require a tedious and time-consuming training procedure to build the decoding model, which can translate EEG signals into commands. In this paper, to address this problem, we propose an adaptive DVI by using a new semi-supervised algorithm. The decoding model of the proposed DVI is first built with a small labeled training set, and then gradually improved by updating the proposed semi-supervised decoding model with new collected unlabeled EEG signals. In our semi-supervised algorithm, independent component analysis (ICA) and Kalman smoother are first used to improve the signal-to-noise ratio (SNR). After that, variational autoencoder is applied to provide a robust feature representation of EEG signals. Finally, a prior information-based transductive support vector machine (PI-TSVM) classifier is developed to translate these features into commands. Experimental results show that the proposed DVI can significantly reduce the training effort. After a short updating, its performance can be close to that of the supervised DVI requiring a lengthy training procedure. This work is vital for advancing the application of these DVIs.

Formation of high-quality vortex laser beams with different orbital angular momenta in the laser resonator.

A scheme for forming high-quality vortex laser beams by employing spiral phase elements inside a laser resonator is presented theoretically. The calculated results show that the purity of the generated mode will decrease slightly as the mode order increases. However, the purity can achieve higher than 0.97 even for the high-order mode. More important, the value of the orbital angular momentum is controllable. Then, the influence of production and alignment errors, including the number of phase levels of the spiral phase elements, the surface roughness of the reflective mirrors, and the decenter of the reflective mirrors, is discussed in detail. The results show that the diffraction loss of the proposed system is more sensitive to production errors, and the purity of the generated mode is more sensitive to alignment errors. Thus, we estimate that the height of one step of the spiral structure should be less than one-fifteenth of the wavelength, the maximum surface fluctuation should be less than one-twentieth of the wavelength, and the vertical distance between the centers of the two reflective mirrors should be less than 20 µm if one wants to obtain high-quality vortex laser beams with high efficiency. The requirements for precision are acceptable for existing microfabrication and operation technologies.

Mathematical Modeling of EEG Signals-Based Brain-Control Behavior.

Brain-control behaviors (BCBs) are behaviors of humans that communicate with external devices by means of the human brain rather than peripheral nerves or muscles. In this paper, to understand and simulate such behaviors, we propose a mathematical model by combining a queuing network-based encoding model with a brain-computer interface model. Experimental results under the static tests show the effectiveness of the proposed model in simulating real BCBs. Furthermore, we verify the effectiveness and applicability of the proposed model through the dynamic experimental tests in a simulated vehicle. This paper not only promotes the understanding and prediction of BCBs, but also provides some insights into assistive technology on brain-controlled systems and extends the scope of research on human behavior modeling.

Queuing Network Modeling of Driver EEG Signals-Based Steering Control.

Directly using brain signals rather than limbs to steer a vehicle may not only help disabled people to control an assistive vehicle, but also provide a complementary means of control for a wider driving community. In this paper, to simulate and predict driver performance in steering a vehicle with brain signals, we propose a driver brain-controlled steering model by combining an extended queuing network-based driver model with a brain-computer interface (BCI) performance model. Experimental results suggest that the proposed driver brain-controlled steering model has performance close to that of real drivers with good performance in brain-controlled driving. The brain-controlled steering model has potential values in helping develop a brain-controlled assistive vehicle. Furthermore, this study provides some insights into the simulation and prediction of the performance of using BCI systems to control other external devices (e.g., mobile robots).

Queuing Network Modeling of Driver EEG Signals-Based Steering Control.

Directly using brain signals rather than limbs to steer a vehicle may not only help disabled people to control an assistive vehicle, but also provide a complementary means of control for a wider driving community. In this paper, to simulate and predict driver performance in steering a vehicle with brain signals, we propose a driver brain-controlled steering model by combining an extended queuing network-based driver model with a brain-computer interface (BCI) performance model. Experimental results suggest that the proposed driver brain-controlled steering model has performance close to that of real drivers with good performance in brain-controlled driving. The brain-controlled steering model has potential values in helping develop a brain-controlled assistive vehicle. Furthermore, this study provides some insights into the simulation and prediction of the performance of using BCI systems to control other external devices (e.g., mobile robots).

Orbit-induced spin squeezing in a spin-orbit coupled Bose-Einstein condensate.

In recent pioneer experiment, a strong spin-orbit coupling, with equal Rashba and Dresselhaus strengths, has been created in a trapped Bose-Einstein condensate. Moreover, many exotic superfluid phenomena induced by this strong spin-orbit coupling have been predicted. In this report, we show that this novel spin-orbit coupling has important applications in quantum metrology, such as spin squeezing. We first demonstrate that an effective spin-spin interaction, which is the heart for producing spin squeezing, can be generated by controlling the orbital degree of freedom (i.e., the momentum) of the ultracold atoms. Compared with previous schemes, this realized spin-spin interaction has advantages of no dissipation, high tunability, and strong coupling. More importantly, a giant squeezing factor (lower than -30 dB) can be achieved by tuning a pair of Raman lasers in current experimental setup. Finally, we find numerically that the phase factor of the prepared initial state affects dramatically on spin squeezing.

A scheme for detecting the atom-field coupling constant in the Dicke superradiation regime using hybrid cavity optomechanical system.

We proposed a scheme for detecting the atom-field coupling constant in the Dicke superradiation regime based on a hybrid cavity optomechanical system assisted by an atomic gas. The critical behavior of the Dicke model was obtained analytically using the spin-coherent-state representation. Without regard to the dynamics of cavity field an analytical formula of one-to-one correspondence between movable mirror's steady position and atom-field coupling constant for a given number of atoms is obtained. Thus the atom-field coupling constant can be probed by measuring the movable mirror's steady position, which is another effect of the cavity optomechanics.