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Using electroencephalography (EEG) signals to drive a vehicle could help disabled people expand their range of motion and improve their independence. A brain-controlled vehicle (BCV) is a vehicle that is commanded by analyzing EEG signals. However, the analysis and transmission effect of EEG signals is not ideal, the driving performance of the BCV solely relying on EEG signals is relatively poor. In this paper, to solve this problem, we propose a dynamic shared control method based on adaptive network-based fuzzy inference system (ANFIS). First, an ANFIS intelligent controller is designed to automatically make decisions according to the state of the vehicle. Then, safety coefficient and intention coefficient are proposed to evaluate the safety and driving intention of the brain-controlled driver. Finally, a fuzzy controller with safety and intention coefficients as inputs and brain-controlled driver weights as outputs is designed. The controller is the embodiment of a human–machine interaction, which allows the driver to maintain maximum control authority over the BCV under safe conditions by dynamically balancing the control authority of the brain-controlled driver and the ANFIS controller on the BCV. To verify the effectiveness of the proposed method, a joint simulation platform of Carsim and Matlab is established, and several groups of comparative simulation experiments are carried out, through which, it is demonstrated that the proposed method can effectively avoid road deviation while well maintaining the control authority of the brain-controlled driver. [ABSTRACT FROM AUTHOR]

A brain-controlled vehicle (BCV) is a vehicle that uses the Brain–Computer interface (BCI) to analyze the driver's electroencephalogram (EEG) to obtain human control commands, which can help disabled people extend movement range and improve their self-independence. At this stage, research on the control of BCVs usually focuses only on the lateral control or longitudinal control, and few studies have focused on the problem of integrated lateral and longitudinal control. Given this situation, this paper investigates the integrated control of brain-controlled vehicles (BCV) in both lateral and longitudinal directions, and proposes a control method that utilizes speed as the coupling point. Firstly, the vehicle's speed is determined by the provided road information. To control the vehicle's longitudinal speed, a hierarchical control method based on model predictive control (MPC) is implemented. This method introduces a threshold σ to speed up the calculation process and reduce resource consumption. Then, a global controller is developed to conduct the motion control. This controller utilizes three local ANFIS intelligent controllers, employing soft-switching combinations, to generate the steering wheel angle signal. The generated steering wheel angle signal is combined with the cornering command issued by the driver. This fusion process produces the final command, which is responsible for the vehicle's motion control. Finally, a simulation model is developed to test the integrated control of the BCV in terms of both lateral and longitudinal movements. Multiple experiments are conducted under varying conditions, and the results prove that the proposed method is able to complete the integrated control of the BCV well, which fully illustrates its effectiveness. [ABSTRACT FROM AUTHOR]

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A brain-controlled vehicle (BCV) is a vehicle that uses the Brain–Computer interface (BCI) to analyze the driver's electroencephalogram (EEG) to obtain human control commands, which can help disabled people extend movement range and improve their self-independence. However, the performance of BCV is poor and the accuracy of BCI recognition varies considerably with different subjects, which severely restrict its promotion and application. To develop a better solution, with the concept of shared control, an intention preferenced fusion brain control strategy based on fuzzy logic is proposed. Firstly, based on Fuzzy Discrete Event System Supervision (FDES) theory, instructions of the driver are supervised and evaluated. At the same time, based on fuzzy logic an automatic controller which gives an automatic decision according to the cur-rent state of the vehicle is designed. Then according to the automatic decision and the correctness of the driver's instructions, a fuzzy reasoning is made, making the final decision more in line with human intentions. In order to verify the effectiveness of the proposed method, based on our previously-designed SSMVEP platform, we conduct virtual experiments, semi-object tests and full physical tests separately for verification. Compared with others shared control schemes, it not only achieves continuous control, but the final control-making is more in line with human intentions, which can encourage users to become more involved, enhancing their self-esteem and brain functions. • An intention preferenced fusion brain control strategy is proposed. • FDES theory is used to supervise the brain instructions. • An automatic controller is designed based on fuzzy logic. • Fuzzy reasoning is used to make the final decision more in line with human intentions. • Virtual experiments, semi-object tests and full physical tests are conducted for verification. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach of detecting emergency braking intention for brain-controlled vehicles by interpreting electroencephalography (EEG) signals of drivers. Regularization linear discriminant analysis with spatial-frequency features is applied to build the detection model. These spatial-frequency features are selected from the powers of frequency points across sixteen channels by using the sequential forward floating search. Experimental results from twelve subjects show that on average, the proposed method can detect emergency braking intentions 420 ms after the onset of emergency situations with the system accuracy of over 94%, showing the feasibility of developing a practical system of detecting driver emergency braking intention with the power spectra of EEG signals for brain-controlled vehicles. [ABSTRACT FROM AUTHOR]

In this work, we developed a novel system to detect the braking intention of drivers in emergency situations using electroencephalogram (EEG) signals. The system acquired eight-channel EEG and motion-sensing data from a custom-designed EEG headset during simulated driving. A novel method for accurately labeling the training data during an extremely short period after the onset of an emergency stimulus was introduced. Two types of features, including EEG band power-based and autoregressive (AR)-based, were investigated. It turned out that the AR-based feature in combination with artificial neural network classifier provided better detection accuracy of the system. Experimental results for ten subjects indicated that the proposed system could detect the emergency braking intention approximately 600 ms before the onset of the executed braking event, with high accuracy of 91%. Thus, the proposed system demonstrated the feasibility of developing a brain-controlled vehicle for real-world applications.

In this paper, we propose a new steady-state visually evoked potential (SSVEP) brain–computer interface (BCI) with visual stimuli presented on a windshield via a head-up display, and we apply this BCI in conjunction with an alpha rhythm to control a simulated vehicle with a 14-DOF vehicle dynamics model. A linear discriminant analysis classifier is applied to detect the alpha rhythm, which is used to control the starting and stopping of the vehicle. The classification models of the SSVEP BCI with three commands (i.e., turning left, turning right, and going forward) are built by using a support vector machine with frequency domain features. A real-time brain-controlled simulated vehicle is developed and tested by using four participants to perform a driving task online, including vehicle starting and stopping, lane keeping, avoiding obstacles, and curve negotiation. Experimental results show the feasibility of using the human “mind” alone to control a vehicle, at least for some users. [ABSTRACT FROM PUBLISHER]

In this work, we developed a novel system to detect the braking intention of drivers in emergency situations using electroencephalogram (EEG) signals. The system acquired eight-channel EEG and motion-sensing data from a custom-designed EEG headset during simulated driving. A novel method for accurately labeling the training data during an extremely short period after the onset of an emergency stimulus was introduced. Two types of features, including EEG band power-based and autoregressive (AR)-based, were investigated. It turned out that the AR-based feature in combination with artificial neural network classifier provided better detection accuracy of the system. Experimental results for ten subjects indicated that the proposed system could detect the emergency braking intention approximately 600 ms before the onset of the executed braking event, with high accuracy of 91%. Thus, the proposed system demonstrated the feasibility of developing a brain-controlled vehicle for real-world applications. [ABSTRACT FROM AUTHOR]