Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios

Vecchiato, Giovanni

doi:10.3389/fnrgo.2021.784827

Cited by 4 publications

(3 citation statements)

References 53 publications

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“…The overall Biohub architecture, including its inter-stream synchronization, was "stress-tested" in the in-car BCI paradigm, which required the online application of advanced machine learning models for time series analysis. The successful findings reported in this test-case indicate the Biohub as a hybrid systemenabling the setting up of paradigms for measuring different psychophysiological variables, with better performance relative to conventional, unimodal BCI systems [20,39].…”

Section: Discussionmentioning

confidence: 87%

Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies

Ascari¹,

Marchenkova

Bellotti³

et al. 2021

Sensors

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Nowadays, the growing interest in gathering physiological data and human behavior in everyday life scenarios is paralleled by an increase in wireless devices recording brain and body signals. However, the technical issues that characterize these solutions often limit the full brain-related assessments in real-life scenarios. Here we introduce the Biohub platform, a hardware/software (HW/SW) integrated wearable system for multistream synchronized acquisitions. This system consists of off-the-shelf hardware and state-of-art open-source software components, which are highly integrated into a high-tech low-cost solution, complete, yet easy to use outside conventional labs. It flexibly cooperates with several devices, regardless of the manufacturer, and overcomes the possibly limited resources of recording devices. The Biohub was validated through the characterization of the quality of (i) multistream synchronization, (ii) in-lab electroencephalographic (EEG) recordings compared with a medical-grade high-density device, and (iii) a Brain-Computer-Interface (BCI) in a real driving condition. Results show that this system can reliably acquire multiple data streams with high time accuracy and record standard quality EEG signals, becoming a valid device to be used for advanced ergonomics studies such as driving, telerehabilitation, and occupational safety.

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Section: Discussionmentioning

confidence: 87%

Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies

Ascari¹,

Marchenkova

Bellotti³

et al. 2021

Sensors

Self Cite

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“…Several studies demonstrated that the combination of EEG and EMG can improve the reliability of movement prediction based on a single modality (Vecchiato 2021 ; Di Liberto et al 2021 ). For example, the prediction of movement onset based on EEG analysis can be improved by designing hybrid systems monitoring at the same time additional peripheral signals depending on the context requirements.…”

Section: Discussionmentioning

confidence: 99%

EEG–EMG coupling as a hybrid method for steering detection in car driving settings

et al. 2022

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Understanding mental processes in complex human behavior is a key issue in driving, representing a milestone for developing user-centered assistive driving devices. Here, we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left and right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings of 128-channel EEG and EMG activity from deltoids and forearm extensors in non-ecological and ecological steering tasks. Specifically, we identified the EEG mu rhythm modulation correlates with motor preparation of self-paced steering actions in the non-ecological task, while the concurrent EMG activity of the left (right) deltoids correlates with right (left) steering. Consequently, we exploited the mu rhythm de-synchronization resulting from the non-ecological task to detect the steering side using cross-correlation analysis with the ecological EMG signals. Results returned significant cross-correlation values showing the coupling between the non-ecological EEG feature and the muscular activity collected in ecological driving conditions. Moreover, such cross-correlation patterns discriminate the steering side earlier relative to the single EMG signal. This hybrid system overcomes the limitation of the EEG signals collected in ecological settings such as low reliability, accuracy, and adaptability, thus adding to the EMG the characteristic predictive power of the cerebral data. These results prove how it is possible to complement different physiological signals to control the level of assistance needed by the driver.

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“…To compare the results obtained with the proposed method, we referred to the following articles [57][58][59][60][61], which focused on the automatic detection of drowsiness and cognitive states for the driver of a vehicle. The comparative results are presented in Table 2.…”

Section: Solution For Viewing and Centralizing Online Data Received From Iot Devicesmentioning

confidence: 99%

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

Minea

Dumitrescu

Costea

2021

Sensors

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Background: The growth of the number of vehicles in traffic has led to an exponential increase in the number of road accidents with many negative consequences, such as loss of lives and pollution. Methods: This article focuses on using a new technology in automotive electronics by equipping a semi-autonomous vehicle with a complex sensor structure that is able to provide centralized information regarding the physiological signals (Electro encephalogram—EEG, electrocardiogram—ECG) of the driver/passengers and their location along with indoor temperature changes, employing the Internet of Things (IoT) technology. Thus, transforming the vehicle into a mobile sensor connected to the internet will help highlight and create a new perspective on the cognitive and physiological conditions of passengers, which is useful for specific applications, such as health management and a more effective intervention in case of road accidents. These sensor structures mounted in vehicles will allow for a higher detection rate of potential dangers in real time. The approach uses detection, recording, and transmission of relevant health information in the event of an incident as support for e-Call or other emergency services, including telemedicine. Results: The novelty of the research is based on the design of specialized non-invasive sensors for the acquisition of EEG and ECG signals installed in the headrest and backrest of car seats, on the algorithms used for data analysis and fusion, but also on the implementation of an IoT temperature measurement system in several points that simultaneously uses sensors based on MEMS technology. The solution can also be integrated with an e-Call system for telemedicine emergency assistance. Conclusion: The research presents both positive and negative results of field experiments, with possible further developments. In this context, the solution has been developed based on state-of-the-art technical devices, methods, and technologies for monitoring vital functions of the driver/passengers (degree of fatigue, cognitive state, heart rate, blood pressure). The purpose is to reduce the risk of accidents for semi-autonomous vehicles and to also monitor the condition of passengers in the case of autonomous vehicles for providing first aid in a timely manner. Reported abnormal values of vital parameters (critical situations) will allow interveneing in a timely manner, saving the patient’s life, with the support of the e-Call system.

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Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios

Cited by 4 publications

References 53 publications

Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies

Validation of a Novel Wearable Multistream Data Acquisition and Analysis System for Ergonomic Studies

EEG–EMG coupling as a hybrid method for steering detection in car driving settings

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

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