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

Wu, Jianshen; Wang, Zhenyu; Xu, Tianheng; Sun, Chengyang

doi:10.3390/s22155631

Cited by 3 publications

(2 citation statements)

References 38 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to its benefits, SSVEP has generated interest in several real-world applications, including operating robots ( Kawase et al, 2017 ), spelling ( Kundu and Ari, 2021 ; Li et al, 2021 ), gaming ( Liao et al, 2012 ), and driving ( Wu et al, 2022 ). However, fatigue is a major challenge in the practical use of SSVEP ( Cao et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Fatigue factors and fatigue indices in SSVEP-based brain-computer interfaces: a systematic review and meta-analysis

Azadi Moghadam,

Maleki

2023

Front. Hum. Neurosci.

View full text Add to dashboard Cite

BackgroundFatigue is a serious challenge when applying a steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) in the real world. Many researchers have used quantitative indices to study the effect of visual stimuli on fatigue. According to a wide range of studies in fatigue analysis, there are contradictions and inconsistencies in the behavior of fatigue indicators.New methodIn this study, for the first time, a systematic review and meta-analysis were performed on fatigue indices and fatigue caused by stimulation paradigm. We queried three scientific search engines for studies published between 2000 and 2022. The inclusion criteria were papers investigating mental and visual fatigue from performing a visual task using electroencephalogram (EEG) signals.ResultsAttractiveness and variation are the most effective ways to reduce BCI fatigue. Therefore, zoom motion, Newton’s ring motion, and cue patterns reduce fatigue. While the color of the cue could effectively reduce fatigue, its shape and background had no effect on fatigue. Additionally, the questionnaire and quantitative indicators such as frequency indices, signal-to-noise ratio (SNR), SSVEP amplitude, and multiscale entropy were utilized to assess fatigue. Meta-analysis indicated that when a person is fatigued, the spectrum amplitude of alpha, theta, and α+θ/β increase significantly, while SNR and SSVEP amplitude decrease significantly.ConclusionThe outcomes of this study can be used to design more optimal stimulation protocols that cause less fatigue. Moreover, the level of fatigue can be quantitatively assessed with indicators without the participant’s self-reports.

show abstract

Section: Introductionmentioning

confidence: 99%

Fatigue factors and fatigue indices in SSVEP-based brain-computer interfaces: a systematic review and meta-analysis

Azadi Moghadam,

Maleki

2023

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…In the energy field, the use of fossil fuels is the main source of global carbon emission [3,4]. As a relatively mature technology that can replace oil-fueled vehicles, the potential of electric vehicles (EVs) in reducing carbon emission and exhaust pollution has gradually emerged [5][6][7]. In the agricultural field, as a basic industry to ensure national food security and ecological security, its high-quality development is also of great significance to the realization of the "dual carbon" goal.…”

Section: Introductionmentioning

confidence: 99%

A Case of Interdisciplinary Fusion under Dual Carbon Goal: Coordinated Carbon Reduction with Greenhouse Photovoltaics and Electric Vehicles

Deng

Zhu

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Building a new type of power system is an important guarantee to support China’s “dual carbon” goal. Due to the inseparable relationship between industrial and agricultural production and electric energy utilization, there must be interdisciplinary integration to achieve the goal of “dual carbon”. The disciplines of horticulture and electric power are taken as examples in this paper to analyze the feasibility of carbon emission reduction through coordinating agricultural photovoltaic (PV) greenhouse and electric vehicle (EV) energy storage. Firstly, the mechanism of carbon emission difference caused by electric energy supplementing during EV charging is analyzed. Secondly, in the context of the contradiction between the reduction of battery life caused by discharging (increasing carbon emission) and the increase in PV output consumption by orderly charging and discharging (reducing carbon emission), an optimization model for the synergistic operation of EV clusters and greenhouse PVs (with the objective of minimizing carbon emissions) is proposed. Finally, the effectiveness of the proposed model is verified through simulation cases. The energy storage characteristics of EVs is capable of realizing the transfer of PV power generation in the time dimension, and the coordinated operation of greenhouse PVs and EVs’ charging and discharging can effectively reduce carbon emission during the EV operation period. In a typical summer scenario of PV output, the carbon emission of EVs in V2G (vehicle to grid) mode was reduced by 69.13% compared to disorderly charging. It is shown that the adequacy of PV generation and the orderly dispatching of the charging and discharging of EVs are the key factors in reducing carbon emission throughout the life cycle of EVs.

show abstract

Optimization of stimulus properties for SSVEP-based BMI system with a heads-up display to control in-vehicle features

Hamidi Shishavan,

Roy,

Golzari

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Over the years, the driver-vehicle interface has been improved, but interacting with in-vehicle features can still increase distraction and affect road safety. This study aims to introduce brain-machine interface (BMI)- based solution to potentially enhance road safety. To achieve this goal, we evaluated visual stimuli properties (SPs) for a steady state visually evoked potentials (SSVEP)-based BMI system. We used a heads-up display (HUD) as the primary screen to present icons for controlling in-vehicle functions such as music, temperature, settings, and navigation. We investigated the effect of various SPs on SSVEP detection performance including the duty cycle and signal-to-noise ratio of visual stimuli, the size, color, and frequency of the icons, and array configuration and location. The experiments were conducted with 10 volunteers and the signals were analyzed using the canonical correlation analysis (CCA), filter bank CCA (FBCCA), and power spectral density analysis (PSDA). Our experimental results suggest that stimuli with a green color, a duty cycle of 50%, presented at a central location, with a size of 36 cm2 elicit a significantly stronger SSVEP response and enhanced SSVEP detection time. We also observed that lower SNR stimuli significantly affect SSVEP detection performance. There was no statistically significant difference observed in SSVEP response between the use of an LCD monitor and a HUD.

show abstract

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

Cited by 3 publications

References 38 publications

Fatigue factors and fatigue indices in SSVEP-based brain-computer interfaces: a systematic review and meta-analysis

Fatigue factors and fatigue indices in SSVEP-based brain-computer interfaces: a systematic review and meta-analysis

A Case of Interdisciplinary Fusion under Dual Carbon Goal: Coordinated Carbon Reduction with Greenhouse Photovoltaics and Electric Vehicles

Optimization of stimulus properties for SSVEP-based BMI system with a heads-up display to control in-vehicle features

Contact Info

Product

Resources

About