Electroencephalographic time-frequency patterns of braking and acceleration movement preparation in car driving simulation

Vecchiato, Giovanni; Vecchio, Maria Del; Ascari, Luca; Antopolskiy, Sergey; Deon, Fabio; Kubin, Luca; Ambeck-Madsen, Jonas; Rizzolatti, Giacomo; Avanzini, Pietro

doi:10.1016/j.brainres.2018.09.004

Cited by 21 publications

(24 citation statements)

References 54 publications

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“…The number of clusters was chosen by a bootstrap-based method which lead to the computation of a stability index (Ben-Hur et al 2002;Salvador and Chan 2004) and the identification of 6 clusters. Details related to the pre-processing pipeline, independent component analysis and clustering can be found in our previous work (Vecchiato et al 2018).…”

Section: Eeg and Emg Data Recording And Pre-processingmentioning

confidence: 99%

“…In the years, several attempts were made to characterize the neural correlates of driving in scenarios with changing level of ecology and different neuroscientific technique investigating haemodynamic (Walter et al 2001;Spiers and Maguire 2007;Mader et al 2009;Calhoun and Pearlson 2012;Schweizer et al 2013), magneto- (Fort et al 2010;Sakihara et al 2014) and electrophysiological (Schier 2000;Haufe et al 2011Haufe et al , 2014Gheorghe et al 2013;Khaliliardali et al 2015; Kim et al 2015;Zhang et al 2015;Brooks and Kerick 2015;Brooks et al 2016;Garcia et al 2017;Vecchiato et al 2018Vecchiato et al , 2020 activity. In particular, these last works show the possibility to use electroencephalography (EEG) to decode the driver's cognitive processes in simulated and real car scenarios with the ultimate goal to predict the upcoming action.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid EEG-EMG system to detect steering actions in car driving settings

Vecchiato

Vecchio

Ambeck-Madsen

et al. 2021

Preprint

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Understanding mental processes in complex human behaviour is a key issue in the context of driving, representing a milestone for developing user-centred assistive driving devices. Here we propose a hybrid method based on electroencephalographic (EEG) and electromyographic (EMG) signatures to distinguish left from right steering in driving scenarios. Twenty-four participants took part in the experiment consisting of recordings 128-channel EEG as well as 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 by means of a 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 left from right steering with an earlier dynamic with respect 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 are a proof of concept of how it is possible to complement different physiological signals to control the level of assistance needed by the driver.

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Section: Eeg and Emg Data Recording And Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid EEG-EMG system to detect steering actions in car driving settings

Vecchiato

Vecchio

Ambeck-Madsen

et al. 2021

Preprint

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“…However, our sample is greater than those of recently published works on mobile EEG. 73,74,76 The gender representation is quite imbalanced, hence it is not possible to carry out any gender-related contrast analysis. We have carried out the data capture sessions inside rooms without working electrical equipment, but we have not ensured total electromagnetic isolation, i.e.…”

Section: T0-mentioning

confidence: 99%

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals

Graña

Aguilar-Moreno

Asiaín

et al. 2020

Int. J. Neur. Syst.

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Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencephalography (EEG) and wireless inertial measurement units (IMU) allow the realization of experimental data recording with improved ecological validity where the subjects can be carrying out natural activities while data recording is minimally invasive. Specifically, we aim to show that EEG and IMU data fusion allows improved human activity recognition in a natural setting. We have defined an experimental protocol composed of natural sitting, standing and walking activities, and we have recruited subjects in two sites: in-house ([Formula: see text]) and out-house ([Formula: see text]) populations with different demographics. Experimental protocol data capture was carried out with validated commercial systems. Classifier model training and validation were carried out with scikit-learn open source machine learning python package. EEG features consist of the amplitude of the standard EEG frequency bands. Inertial features were the instantaneous position of the body tracked points after a moving average smoothing to remove noise. We carry out three validation processes: a 10-fold cross-validation process per experimental protocol repetition, (b) the inference of the ethograms, and (c) the transfer learning from each experimental protocol repetition to the remaining repetitions. The in-house accuracy results were lower and much more variable than the out-house sessions results. In general, random forest was the best performing classifier model. Best cross-validation results, ethogram accuracy, and transfer learning were achieved from the fusion of EEG and IMUs data. Transfer learning behaved poorly compared to classification on the same protocol repetition, but it has accuracy still greater than 0.75 on average for the out-house data sessions. Transfer leaning accuracy among repetitions of the same subject was above 0.88 on average. Ethogram prediction accuracy was above 0.96 on average. Therefore, we conclude that wireless EEG and IMUs allow for the definition of natural experimental designs with high ecological validity toward human computational neuroethology research. The fusion of both EEG and IMUs signals improves activity and ethogram recognition.

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“…The experimental results showed in Teng and Bi (2017), Teng et al (2015) indicated that the system could issue a braking command 400 ms earlier than drivers, whereas, in Gougeh et al (2021), the system was able to classify three classes with high accuracy and moreover commands could be predicted 500 ms earlier. In Vecchiato et al (2019), it was established that dorso-mesial premotor cortex has involvement in the preparation of foot movement for braking and acceleration actions. The error (related) negativity (Ne/ERN) is an event-related potential in the electroencephalogram correlating with error processing.…”

Section: Introductionmentioning

confidence: 99%

Augmenting robot intelligence via EEG signals to avoid trajectory planning mistakes of a smart wheelchair

Ferracuti

Freddi

Iarlori

et al. 2021

J Ambient Intell Human Comput

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Assistive robots operate in complex environments and in presence of human beings, but the interaction between them can be affected by several factors, which may lead to undesired outcomes: wrong sensor readings, unexpected environmental conditions, or algorithmic errors represent just a few examples of the possible scenarios. When the safety of the user is not only an option but must be guaranteed, a feasible solution is to rely on a human-in-the-loop approach, e.g., to monitor if the robot performs a wrong action during a task execution or environmental conditions affect safety during the human-robot interaction, and provide a feedback accordingly. The present paper proposes a human-in-the-loop framework to enable safe autonomous navigation of an electric powered and sensorized (smart) wheelchair. During the wheelchair navigation towards a desired destination in an indoor scenario, possible problems (e.g. obstacles) along the trajectory cause the generation of electroencephalography (EEG) potentials when noticed by the user. These potentials can be used as additional inputs to the navigation algorithm in order to modify the trajectory planning and preserve safety. The framework has been preliminarily tested by using a wheelchair simulator implemented in ROS and Gazebo environments: EEG signals from a benchmark known in the literature were classified, passed to a custom simulation node, and made available to the navigation stack to perform obstacle avoidance.

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Electroencephalographic time-frequency patterns of braking and acceleration movement preparation in car driving simulation

Cited by 21 publications

References 54 publications

Hybrid EEG-EMG system to detect steering actions in car driving settings

Hybrid EEG-EMG system to detect steering actions in car driving settings

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals

Augmenting robot intelligence via EEG signals to avoid trajectory planning mistakes of a smart wheelchair

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