Queuing Network Modeling of Driver EEG Signals-Based Steering Control

A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials

In this paper, we propose a human-vehicle cooperative driving system. The objectives of this research are twofold: (1) providing a feasible brain-controlled vehicle (BCV) mode; (2) providing a human-vehicle cooperative control mode. For the first aim, through a brain-computer interface (BCI), we can analyse the EEG signal and get the driving intentions of the driver. For the second aim, the human-vehicle cooperative control is manifested in the BCV combined with the obstacle detection assistance. Considering the potential dangers of driving a real motor vehicle in the outdoor, an obstacle detection module is essential in the human-vehicle cooperative driving system. Obstacle detection and emergency braking can ensure the safety of the driver and the vehicle during driving. EEG system based on steady-state visual evoked potential (SSVEP) is used in the BCI. Simulation and real vehicle driving experiment platform are designed to verify the feasibility of the proposed human-vehicle cooperative driving system. Five subjects participated in the simulation experiment and real the vehicle driving experiment. The outdoor experimental results show that the average accuracy of intention recognition is 90.68 ± 2.96% on the real vehicle platform. In this paper, we verified the feasibility of the SSVEP-based BCV mode and realised the human-vehicle cooperative driving system.

A Bibliometric Analysis of Human-Machine Interaction Methodology for Electric-Powered Wheelchairs Driving from 1998 to 2020

Electric power wheelchairs (EPWs) enhance the mobility capability of the elderly and the disabled, while the human-machine interaction (HMI) determines how well the human intention will be precisely delivered and how human-machine system cooperation will be efficiently conducted. A bibliometric quantitative analysis of 1154 publications related to this research field, published between 1998 and 2020, was conducted. We identified the development status, contributors, hot topics, and potential future research directions of this field. We believe that the combination of intelligence and humanization of an EPW HMI system based on human-machine collaboration is an emerging trend in EPW HMI methodology research. Particular attention should be paid to evaluating the applicability and benefits of the EPW HMI methodology for the users, as well as how much it contributes to society. This study offers researchers a comprehensive understanding of EPW HMI studies in the past 22 years and latest trends from the evolutionary footprints and forward-thinking insights regarding future research.

Modeling of Human Operator Behavior for Brain-Actuated Mobile Robots Steering

Human operator control of brain-actuated robot steering based on electroencephalograph (EEG)-signals is a complex behavior consisting of surroundings perceiving, decision making, and commands issuing and differs among individual operators. However, no existing models allow decoupling the user from the loop to improve the system design and testing process, which can capture such behavior of a brain-actuated robot. To address this problem, in this paper, we propose an operator brain-controlled steering model consisting of an operator decision model based on the queuing network (QN) cognitive architecture and a brain-machine interface (BMI) performance model. The QN-based operator decision model can mimic the human decision process with the individual operator differences considered. The new BMI performance model is built to represent the varied accuracy of BMI during brain-controlled direction operations. Furthermore, the model is simulated and validated against the results of human operator-in-the-loop experiments. The results show that the proposed model can reproduce the behavior of human operators thanks to its similar direction control performance.

Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model

Due to growth in population, Individual persons with disabilities are increasing daily. To overcome the disability especially in Locked in State (LIS) due to Spinal Cord Injury (SCI), we planned to design four states moving robot from four imagery tasks signals acquired from three electrode systems by placing the electrodes in three positions namely T1, T3 and FP1. At the time of the study we extract the features from Continuous Wavelet Transform (CWT) and trained with Optimized Neural Network model to analyze the features. The proposed network model showed the highest performances with an accuracy of 93.86 % then that of conventional network model. To confirm the performances we conduct offline test. The offline test also proved that new network model recognizing accuracy was higher than the conventional network model with recognizing accuracy of 97.50 %. To verify our result we conducted Information Transfer Rate (ITR), from this analysis we concluded that optimized network model outperforms the other network models like conventional ordinary Feed Forward Neural Network, Time Delay Neural Network and Elman Neural Networks with an accuracy of 21.67 bits per sec. By analyzing classification performances, recognizing accuracy and Information Transformation Rate (ITR), we concluded that CWT features with optimized neural network model performances were comparably greater than that of normal or conventional neural network model and also the study proved that performances of male subjects was appreciated compared to female subjects.

EEG Signals-Based Longitudinal Control System for a Brain-Controlled Vehicle

Directly using brain signals to drive a vehicle may not only help persons with disabilities to regain driving ability but also provide a new alternative way for healthy people to control a vehicle. In this paper, we propose a new longitudinal control system based on electroencephalogram signals for brain-controlled vehicles (BCVs) by combining a user interface, a brain-computer interface (BCI) system, and a longitudinal control module. Driver-in-the-loop experiments were conducted by using two driving tests (i.e., the destination-approaching and car-following tests) with different subjects under two control conditions, i.e., the brain and manual control conditions. Experimental results show the feasibility of alone using brain signals to continuously perform the longitudinal control of a vehicle at a relatively high speed, at least for some users. This paper not only promotes the development of BCVs but also provides some insights into the research on how to apply BCIs to control other high-speed dynamic systems.

A Novel Method of Emergency Situation Detection for a Brain-Controlled Vehicle by Combining EEG Signals With Surrounding Information

In this paper, to address the safety of brain-controlled vehicles under emergency situations, we propose a novel method of emergency situation detection by fusing driver electroencephalography (EEG) signals with surrounding information. We first build a novel EEG-based detection model of driver emergency braking intention. We then recognize emergency situations by fusing the result of the proposed EEG-based intention detection model with that of the obstacle detection model based on surrounding information. The real-time detection system of driver emergency braking intention is implemented on an embedded system, and the driver-and-hardware-in-the-loop-experiment of the proposed detection method of emergency situations is performed. Experimental results show that the proposed method can detect emergency situations with the system accuracy of 94.89%, false alarm rate of 0.05%, and response time of 540 ms. This paper has important values in the future development of brain-controlled vehicles, human-centric advanced driver assistant systems, and self-driving vehicles and opens a new avenue on how cognitive neuroscience may be applied to human-machine integration.

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.