Quadcopter Flight Control Using a Non-invasive Multi-Modal Brain Computer Interface

Brain-Computer Interfaces (BCIs) translate neuronal information into commands to control external software or hardware, which can improve the quality of life for both healthy and disabled individuals. Here, a multi-modal BCI which combines motor imagery (MI) and steady-state visual evoked potential (SSVEP) is proposed to achieve stable control of a quadcopter in three-dimensional physical space. The complete information common spatial pattern (CICSP) method is used to extract two MI features to control the quadcopter to fly left-forward and right-forward, and canonical correlation analysis (CCA) is employed to perform the SSVEP classification for rise and fall. Eye blinking is designed to switch these two modes while hovering. Real-time feedback is provided to subjects by a global camera. Two flight tasks were conducted in physical space in order to certify the reliability of the BCI system. Subjects were asked to control the quadcopter to fly forward along the zig-zag pattern to pass through a gate in the relatively simple task. For the other complex task, the quadcopter was controlled to pass through two gates successively according to an S-shaped route. The performance of the BCI system is quantified using suitable metrics and subjects are able to acquire 86.5% accuracy for the complicated flight task. It is demonstrated that the multi-modal BCI has the ability to increase the accuracy rate, reduce the task burden, and improve the performance of the BCI system in the real world

A Fiber-Optic Electrochemilunescence Sensor Placed into Sample Solution

A novel fiber-optic electrochemiluninescence sensor has been developed. It can be conveniently applied by placing into the sample solution in a manner similar to a conventional ion selective electrode, without requiring liquid luminous reagent and a flowing system. The sensor is consisted of an independent probe and a case for exclusion the ambient light, allowing the sample solution to pass through. Within the body of the probe, an optical fiber is utilized to collect and transmit light signal. In the three electrode configuration of the probe, a Pt electrode coated with Ru(bpy)32+- modified chitosan/silica gel membrane is used to give the electrolytic potential and provide a selective luminous membrane. The linear responses of the sensor to oxalic acid and amino acid were obtained in the concentration range of 2.0 × 10-4 to 1.0 × 10 –2 mol/dm3 with the relative standard deviations of 3.5% and 5.7%, respectively. The response of the sensor was not less than 80% of the initial value after the service time was over one month

An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training

Brain–computer interface (BCI) has developed rapidly over the past two decades, mainly due to advancements in machine learning. Subjects must learn to modulate their brain activities to ensure a successful BCI. Feedback training is a practical approach to this learning process; however, the commonly used classifier-dependent approaches have inherent limitations such as the need for calibration and a lack of continuous feedback over long periods of time. This paper proposes an online data visualization feedback protocol that intuitively reflects the EEG distribution in Riemannian geometry in real time. Rather than learning a hyperplane, the Riemannian geometry formulation allows iterative learning of prototypical covariance matrices that are translated into visualized feedback through diffusion map process. Ten subjects were recruited for MI-BCI (motor imagery-BCI) training experiments. The subjects learned to modulate their sensorimotor rhythm to centralize the points within one category and to separate points belonging to different categories. The results show favorable overall training effects in terms of the class distinctiveness and EEG feature discriminancy over a 3-day training with 30% learners. A steadily increased class distinctiveness in the last three sessions suggests that the advanced training protocol is effective. The optimal frequency band was consistent during the 3-day training, and the difference between subjects with good or low MI-BCI performance could be clearly observed. We believe that the proposed feedback protocol has promising application prospect

A Fiber-Optic Electrochemilunescence Sensor Placed into Sample Solution

Abstract: A novel fiber-optic electrochemiluninescence sensor has been developed. It can be conveniently applied by placing into the sample solution in a manner similar to a conventional ion selective electrode, without requiring liquid luminous reagent and a flowing system. The sensor is consisted of an independent probe and a case for exclusion the ambient light, allowing the sample solution to pass through. Within the body of the probe, an optical fiber is utilized to collect and transmit light signal. In the three electrode configuration of the probe, a Pt electrode coated with Ru(bpy)3 2+- modified chitosan/silica gel membrane is used to give the electrolytic potential and provide a selective luminous membrane. The linear responses of the sensor to oxalic acid and amino acid were obtained in the concentration range of 2.0 × 10-4 to 1.0 × 10 –2 mol/dm 3 with the relative standard deviations of 3.5 % and 5.7%, respectively. The response of the sensor was not less than 80 % of the initial value after the service time was over one month

Exploring the Temporal Patterns of Dynamic Information Flow during Attention Network Test (ANT)

The attentional processes are conceptualized as a system of anatomical brain areas involving three specialized networks of alerting, orienting and executive control, each of which has been proven to have a relation with specified time-frequency oscillations through electrophysiological techniques. Nevertheless, at present, it is still unclear how the idea of these three independent attention networks is reflected in the specific short-time topology propagation of the brain, assembled with complexity and precision. In this study, we investigated the temporal patterns of dynamic information flow in each attention network via electroencephalograph (EEG)-based analysis. A modified version of the attention network test (ANT) with an EEG recording was adopted to probe the dynamic topology propagation in the three attention networks. First, the event-related potentials (ERP) analysis was used to extract sub-stage networks corresponding to the role of each attention network. Then, the dynamic network model of each attention network was constructed by post hoc test between conditions followed by the short-time-windows fitting model and brain network construction. We found that the alerting involved long-range interaction among the prefrontal cortex and posterior cortex of brain. The orienting elicited more sparse information flow after the target onset in the frequency band 1–30 Hz, and the executive control contained complex top-down control originating from the frontal cortex of the brain. Moreover, the switch of the activated regions in the associated time courses was elicited in attention networks contributing to diverse processing stages, which further extends our knowledge of the mechanism of attention networks