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

Hekmatmanesh, Amin; Nardelli, Pedro H. J.; Handroos, Heikki

doi:10.1109/access.2021.3100700

Cited by 45 publications

(32 citation statements)

References 79 publications

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“…The relevance of this review within the context of these other review papers is then discussed. The relevant review papers were: Hekmatmanesh, 2021 [ 7 ]: Focus on brain-controlled vehicles, covering exogenous paradigms and endogenous paradigms. Wang, 2021 [ 8 ]: Review of BCI-controlled wheelchair systems, including electrode type, modality, and synchronicity.…”

Section: Relevance Of This Reviewmentioning

confidence: 99%

“…Hekmatmanesh, 2021 [ 7 ]: Focus on brain-controlled vehicles, covering exogenous paradigms and endogenous paradigms.…”

Section: Relevance Of This Reviewmentioning

confidence: 99%

“…The review paper by Hekmatmanesh et al [ 7 ] was largely focused on brain-controlled vehicles, whereas our review paper also discusses prosthetics, robotic arms, exoskeletons, and pedaling machines at length. The paper by Hekmatmanesh et al [ 7 ] had a significant focus on the feature-extraction and classification techniques used, but some deep-learning-based systems were not discussed at length [ 2 , 12 ], whereas our paper discusses several deep-learning-based approaches in depth.…”

Section: Relevance Of This Reviewmentioning

confidence: 99%

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A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

Padfield

Camilleri

et al. 2022

Sensors

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Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work.

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Section: Relevance Of This Reviewmentioning

confidence: 99%

“…Hekmatmanesh, 2021 [ 7 ]: Focus on brain-controlled vehicles, covering exogenous paradigms and endogenous paradigms.…”

Section: Relevance Of This Reviewmentioning

confidence: 99%

Section: Relevance Of This Reviewmentioning

confidence: 99%

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A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

Padfield

Camilleri

et al. 2022

Sensors

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“…3. Autonomous-driving vehicles —BCI control of autonomous vehicles is increasingly considered for healthy users (Rehman et al, 2018 ; Chai et al, 2021 ; Hekmatmanesh et al, 2021 ). Such BCI presented by Mercedes-Benz in their concept car (Rosso, 2021 ) enabled “selecting the navigation destination by thought control, switching the ambient light in the interior or changing the radio station” (Mercedes-Benz VISION AVTR, 2021 ).…”

Section: Applicationsmentioning

confidence: 99%

Active Brain-Computer Interfacing for Healthy Users

Shishkin

2022

Front. Neurosci.

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“…Additionally, the literature also investigated brain-controlled vehicles from the perspective of action and brake detection [ 21 , 22 , 23 ], lateral and longitudinal control [ 24 ], a collaborative control system incorporating computer vision and radar, etc. In addition, a comprehensive review is given in [ 25 ], where brain-controlled vehicles and aerial vehicles are introduced in details. However, research on brain-controlled vehicles is still in its early stages and many important problems remain unsolved, among which we find brain-controlled driving mode selection.…”

Section: Introductionmentioning

confidence: 99%

Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis

Wang

et al. 2022

Sensors

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Background: The brain–computer interface (BCI) is a highly cross-discipline technology and its successful application in various domains has received increasing attention. However, the BCI-enabled automobile industry is has been comparatively less investigated. In particular, there are currently no studies focusing on brain-controlled driving mode selection. Specifically, different driving modes indicate different driving styles which can be selected according to the road condition or the preference of individual drivers. Methods: In this paper, a steady-state visual-evoked potential (SSVEP)-based driving mode selection system is proposed. Upon this system, drivers can select the intended driving modes by only gazing at the corresponding SSVEP stimuli. A novel EEG processing algorithm named inter-trial distance minimization analysis (ITDMA) is proposed to enhance SSVEP detection. Both offline and real-time experiments were carried out to validate the effectiveness of the proposed system. Conclusion: The results show that a high selection accuracy of up to 92.3% can be realized, although this depends on the specific choice of flickering duration, the number of EEG channels, and the number of training signals. Additionally, energy consumption is investigated in terms of which the proposed brain-controlled system considerably differs from a traditional driving mode selection system, and the main reason is shown to be the existence of a detection error.

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

Cited by 45 publications

References 79 publications

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control

Active Brain-Computer Interfacing for Healthy Users

Driving Mode Selection through SSVEP-Based BCI and Energy Consumption Analysis

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