Ensemble Learning Based Brain–Computer Interface System for Ground Vehicle Control

Zhuang, Jiayu; Geng, Keke; Yin, Guodong

doi:10.1109/tsmc.2019.2955478

Cited by 32 publications

(38 citation statements)

References 55 publications

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“…Despite these challenges, several studies have demonstrated the feasibility of BCVs. We can mention, for instance, control of a vehicle in four main directions [107], [108], methods for obstacle avoidance [109], [110], and hand brake assistance in emergency situations [111], [112], using diverse platforms such as vehicle simulators, virtual reality vehicles, vehicles in video games, quadcopters, drones, helicopters, and fixed-wings aircrafts. A general simulator-based procedure for training a participant is shown in Fig.…”

Section: A State-of-the-artmentioning

confidence: 99%

Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

Moioli

Nardelli

Barros

et al. 2021

IEEE Commun. Surv. Tutorials

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This paper presents the first comprehensive tutorial on a promising research field located at the frontier of two wellestablished domains, neurosciences and wireless communications, motivated by the ongoing efforts to define the Sixth Generation of Mobile Networks (6G). In particular, this tutorial first provides a novel integrative approach that bridges the gap between these two seemingly disparate fields. Then, we present the state-ofthe-art and key challenges of these two topics. In particular, we propose a novel systematization that divides the contributions into two groups, one focused on what neurosciences will offer to future wireless technologies in terms of new applications and systems architecture (Neurosciences for Wireless Networks), and the other on how wireless communication theory and nextgeneration wireless systems can provide new ways to study the brain (Wireless Networks for Neurosciences). For the first group, we explain concretely how current scientific understanding of the brain would enable new applications within the context of a new type of service that we dub brain-type communications and that has more stringent requirements than human-and machine-type communication. In this regard, we expose the key requirements of brain-type communication services and discuss how future wireless networks can be equipped to deal with such services. Meanwhile, for the second group, we thoroughly explore modern communication systems paradigms, including Internet of Bio-Nano Things and wireless-integrated brainmachine interfaces, in addition to highlighting how complex systems tools can help bridging the upcoming advances of wireless technologies and applications of neurosciences. Brain-controlled vehicles are then presented as our case study to demonstrate for both groups the potential created by the convergence of neurosciences and wireless communications, probably in 6G. In summary, this tutorial is expected to provide a largely missing articulation between neurosciences and wireless communications while delineating concrete ways to move forward in such an interdisciplinary endeavor.

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Section: A State-of-the-artmentioning

confidence: 99%

Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

Moioli

Nardelli

Barros

et al. 2021

IEEE Commun. Surv. Tutorials

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“…In order to control a vehicle by using bio-signals, different simulators and algorithms have been used as illustrated in Tables 1 and 2. Studies published on BCV and BCAV topics are related to detection of the driver's intentions to control a vehicle for navigation, changing the lane, steering control, [20], [21], the EBC [22], [23], and the OAC [13], [24]. The studies discussed here are divided into two parts; BCV and BCAV studies, which are organized into successful initial ideas (exploring patterns and how to generate patterns by using appropriate tasks), mathematical developments, and improvements to the current situation step by step.…”

Section: Studies On Bcv and Bcavmentioning

confidence: 99%

“…Next, Zhuang et al [23] implemented an EEG-based algorithm with real-time visual feedback to control a simulated vehicle for controlling a BCV in three states of right and left steering and acceleration for the OAC task. Zhuang et al employed a combination of wavelet and Canonical Correlation Analysis (CCA) to reveal the ERD/ERS patterns.…”

Section: A Techniques Employed For Bcv Applications and Their Efficienciesmentioning

confidence: 99%

“…The EC is known as a generalized approach to increase the efficiency of any classifiers in comparison with individual classifiers, such as boosting and bagging methods. Boosting is a popular method in the EC for reducing bias to obtain a strong dependence in the data [23]. The drawbacks of the study is the use of a time-consuming algorithm, called wavelet, which causes delay in real-time systems.…”

Section: A Techniques Employed For Bcv Applications and Their Efficienciesmentioning

confidence: 99%

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Review of the State-of-the-Art of Brain-Controlled Vehicles

2021

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“…During the last decade, a wide range of machine learning approaches has been proposed to classify the motor imagery EEG signal [17, 20, 20, 22]. In real-time applications, the reported prediction performance has remained relatively low due to the high dimensionality and dynamic behaviors of the real-time EEG data [4, 23, 21, 24]. In the MI EEG signal classification study, Kumar et al [25] employed a mutual information-based frequency band selection approach to utilize all information that is got from different channels.…”

Section: Related Workmentioning

confidence: 99%

CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data

Miah¹,

Muhammod²,

Mamun³

et al. 2021

Preprint

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Background: The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal task in the biosignal classification process in brain-computer interface (BCI) applications. Currently, this bio-engineering-based technology is being employed by researchers in various fields to develop cutting-edge applications. The classification of real-time MI-EEG signals is the most challenging task in these applications. The prediction performance of the existing classification methods is still limited due to the high dimensionality and dynamic behaviors of the real-time EEG data. Proposed Method: To enhance the classification performance of real-time BCI applications, this paper presents a new clustering-based ensemble technique called CluSem to mitigate this problem. We also develop a new brain game called CluGame using this method to evaluate the classification performance of real-time motor imagery movements. In this game, real-time EEG signal classification and prediction tabulation through animated balls are controlled via threads. By playing this game, users can control the movements of the balls via the brain signals of motor imagery movements without using any traditional input devices. Results: Our results demonstrate that CluSem is able to improve the classification accuracy between 5% and 15% compared to the existing methods on our collected as well as the publicly available EEG datasets. The source codes used to implement CluSem and CluGame are publicly available at https://github.com/MdOchiuddinMiah/MI-BCI_ML.

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Ensemble Learning Based Brain–Computer Interface System for Ground Vehicle Control

Cited by 32 publications

References 55 publications

Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

Neurosciences and Wireless Networks: The Potential of Brain-Type Communications and Their Applications

Review of the State-of-the-Art of Brain-Controlled Vehicles

CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data

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