Multitask-Oriented Brain-Controlled Intelligent Vehicle Based on Human–Machine Intelligence Integration

Wang, Jiarong; Bi, Luzheng; Fei, Weijie

doi:10.1109/tsmc.2022.3212744

Cited by 15 publications

(10 citation statements)

References 31 publications

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“…Additionally, a designated ground electrode was placed at the AFz position on the forehead, serving as a stable reference point to further improve the signal-noise ratio. Electrode impedances were maintained below 10 kΩ, and signal amplification was conducted using a portable wireless EEG amplifier (NeuSen.W64, Neuracle, Changzhou, China) with a sampling rate of 1000 Hz [20][21][22][23].…”

Section: Methodsmentioning

confidence: 99%

The EEG-Based Fusion Entropy-Featured Identification of Isometric Contraction Forces under the Same Action

Yao,

Wu,

Zhang

et al. 2024

Sensors

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This study explores the important role of assessing force levels in accurately controlling upper limb movements in human–computer interfaces. It uses a new method that combines entropy to improve the recognition of force levels. This research aims to differentiate between different levels of isometric contraction forces using electroencephalogram (EEG) signal analysis. It integrates eight different entropy measures: power spectrum entropy (PSE), singular spectrum entropy (SSE), logarithmic energy entropy (LEE), approximation entropy (AE), sample entropy (SE), fuzzy entropy (FE), alignment entropy (PE), and envelope entropy (EE). The findings emphasize two important advances: first, including a wide range of entropy features significantly improves classification efficiency; second, the fusion entropy method shows exceptional accuracy in classifying isometric contraction forces. It achieves an accuracy rate of 91.73% in distinguishing between 15% and 60% maximum voluntary contraction (MVC) forces, along with 69.59% accuracy in identifying variations across 15%, 30%, 45%, and 60% MVC. These results illuminate the efficacy of employing fusion entropy in EEG signal analysis for isometric contraction detection, heralding new opportunities for advancing motor control and facilitating fine motor movements through sophisticated human–computer interface technologies.

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Section: Methodsmentioning

confidence: 99%

The EEG-Based Fusion Entropy-Featured Identification of Isometric Contraction Forces under the Same Action

Yao,

Wu,

Zhang

et al. 2024

Sensors

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show abstract

“…Up to now, the neural correlates and movement decoding were analyzed using offline data. In future work, an online test with two manipulators could be done to develop a more natural bimanual-coordination BCI for neurorehabilitation and daily assistance, and intelligent assistive technologies could be introduced to enhance the online performance [31].…”

Section: E Limitations and Future Workmentioning

confidence: 99%

Neural Correlate and Movement Decoding of Simultaneous-and-Sequential Bimanual Movements Using EEG Signals

Wang,

Bi,

Fei

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Bimanual coordination is important for developing a natural motor brain-computer interface (BCI) from electroencephalogram (EEG) signals, covering the aspects of bilateral arm training for rehabilitation, bimanual coordination for daily-life assistance, and also improving the multidimensional control of BCIs. For the same task targets of both hands, simultaneous and sequential bimanual movements are two different bimanual coordination manners. Planning and performing motor sequences are the fundamental abilities of humans, and it is more natural to execute sequential movements compared to simultaneous movements in many complex tasks. However, to date, for these two different manners in which two hands coordinated to reach the same task targets, the differences in the neural correlate and also the feasibility of movement discrimination have not been explored. In this study, we aimed to investigate these two issues based on a bimanual reaching task for the first time. Finally, neural correlates in the view of the movement-related cortical potentials, event-related oscillations, and source imaging showed unique neural encoding patterns of sequential movements. Besides, for the same task targets of both hands, the simultaneous and sequential bimanual movements were successfully discriminated in both pre-movement and movement execution periods. This study revealed the neural encoding patterns of sequential bimanual movements and presented its values in developing a more natural and good-performance motor BCI.

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“…ITS utilizing vehicle-to-everything (V2X) technology [6] meet the demands of modern transport systems [7][8][9] by continuously collecting data on road traffic through various sensors [4]. The collected and systematized data, which represent the context of traffic situations, are crucial for determining the evolving requirements of ITS functionality over time [10].…”

Section: Implementation Of Intelligent Transport Systems Via Simulati...mentioning

confidence: 99%

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

Lopukhova,

Abdulnagimov,

Voronkov

et al. 2024

Applied Sciences

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Accurate traffic simulation models play a crucial role in developing intelligent transport systems that offer timely traffic information to users and efficient traffic management. However, calibrating these models to represent real-world traffic conditions accurately poses a significant challenge due to the dynamic nature of traffic flow and the limitations of traditional calibration methods. This article introduces a machine learning-based approach to calibrate macroscopic traffic simulation models using real-time traffic video stream data. The proposed method for creating and calibrating a traffic simulation model has significantly improved the statistical correspondence between the generated vehicle characteristics and real data about cars on the simulated road section. The correspondence has increased from 37% to 73%. Machine learning models trained on generated data and tested on real data show improved accuracy rates. Mean absolute error, mean square error, and mean absolute percentage error decreased by more than two orders of magnitude. The coefficient of determination has also increased, approaching 1. This method eliminates the need to deploy wireless sensor networks, which can reduce the cost of implementing intelligent transport systems.

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Multitask-Oriented Brain-Controlled Intelligent Vehicle Based on Human–Machine Intelligence Integration

Cited by 15 publications

References 31 publications

The EEG-Based Fusion Entropy-Featured Identification of Isometric Contraction Forces under the Same Action

The EEG-Based Fusion Entropy-Featured Identification of Isometric Contraction Forces under the Same Action

Neural Correlate and Movement Decoding of Simultaneous-and-Sequential Bimanual Movements Using EEG Signals

Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis

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