Publication

BCI based on Neural Correlates of Anticipatory Behavior during Driving

Zahra Khaliliardali
2016
EPFL thesis
Abstract

Anticipation of events such as, changes in traffic light signals and preparing to brake or accelerate are critical behaviors during driving. Smart vehicles, equipped with on-board Brain-Computer Interface (BCI), could decode the driver's intention to perform an action from his brain activity, thus enriching the interaction with its driver. To this end, the contribution of this thesis are three fold: (i) it presents 3 experiments to investigate anticipatory behavior from Electroencephalogram (EEG), while driving: count-down paradigm, traffic light changes in a virtual city and on a real road, (ii) it proposes methods for synchronous single-trial EEG classification of this behavior as well as asynchronous detection of movement intention, (iii) it explores new filtering techniques toward online application. In the first part, we present our first experiment, inspired from the classical Contingent Negative Variation (CNV) paradigm, where a count-down of numbers predicted the appearance of a cue that instructed to brake or accelerate accordingly. Through the EEG data (N=18), we show the presence of anticipatory potentials locked to the stimuli onset, which are similar to the well-known central negative Slow Cortical Potentials (SCPs). We further demonstrate the discrimination between cases requiring an action (brake/accelerate) upon an imperative subsequent stimulus (Start'/Stop' cues) vs. the events that do not require such action (count-down cues). We also show the possibility of detecting driver's movement intention through these potentials. In the second part, we extend the study to a more realistic scenarios using traffic lights through next two experiments. For the second experiment, we recorded 10 subjects over 3 days in a car simulator. During the second and third day, the subjects received online classification feedback together with reaction time after braking. Through this data, we confirm the presence of the anticipatory SCPs in response to the traffic lights as well as offline single trial performances with similar patterns to those of the first experiment. Interestingly, for the brake trials, we observed an improvement in the anticipatory behavior, which is likely due to the feedback provision. In the real car experiment on a closed road, we recorded EEG (N=8) over 2 days. Remarkably, we confirmed the existence of the anticipatory SCPs and demonstrated the possibility of detecting these potentials, despite large amounts of driving related visual distractions and movement artifacts. Thirdly, we report a post-hoc analysis on investigating the influence of filtering on the SCP detection performance. We present a new spectral filtering method, called Predictive Cascade Filter (PCF), which theoretically reduced the group delay associated with filtering of low frequency bands. The grand averages illustrate this reduction, whereas the classification performance did not improve by using the PCF filters. Indeed, the lowpass PCF as well as the usual lowpass filter appeared to be the best when applied causally (pertinent to online application), whereas the bandpass filter performed best, when applied non-causally (pertinent to offline analysis). We believe, the contributions presented in this thesis can impact the advancement of neuro-technology into smart vehicles as well as other applications such as neuro-rehabilitation.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related concepts (35)
Electroencephalography
Electroencephalography (EEG) is a method to record an electrogram of the spontaneous electrical activity of the brain. The biosignals detected by EEG have been shown to represent the postsynaptic potentials of pyramidal neurons in the neocortex and allocortex. It is typically non-invasive, with the EEG electrodes placed along the scalp (commonly called "scalp EEG") using the International 10–20 system, or variations of it. Electrocorticography, involving surgical placement of electrodes, is sometimes called "intracranial EEG".
Neural oscillation
Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons.
Neurofeedback
Neurofeedback is a type of biofeedback that focuses on the neuronal activity of the brain. The training method is based on reward learning (operant conditioning) where a real-time feedback provided to the trainee is supposed to reinforce desired brain activity or inhibit unfavorable activity patterns. Different mental states (for example, concentration, relaxation, creativity, distractibility, rumination, etc.) are associated with different brain activities or brain states.
Show more
Related publications (69)

Who says you are so sick? An investigation on individual susceptibility to cybersickness triggers using EEG, EGG and ECG

Ronan Boulic, Nana Tian

In this research paper, we conducted a study to investigate the connection between three objective measures: Electrocardiogram(ECG), Electrogastrogram (EGG), and Electroencephalogram (EEG), and individuals' susceptibility to cybersickness. Our primary obje ...
2024

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

Silvestro Micera, Michael Lassi

Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals. Approach. EEG recordings of 17 heal ...
IOP Publishing Ltd2023

Breathing is coupled with voluntary initiation of mental imagery

Olaf Blanke, Oliver Alan Kannape, Hyeongdong Park

Previous research has suggested that bodily signals from internal organs are associated with diverse cortical and subcortical processes involved in sensory-motor functions, beyond homeostatic reflexes. For instance, a recent study demonstrated that the pre ...
ACADEMIC PRESS INC ELSEVIER SCIENCE2022
Show more
Related MOOCs (20)
Neuronal Dynamics - Computational Neuroscience of Single Neurons
The activity of neurons in the brain and the code used by these neurons is described by mathematical neuron models at different levels of detail.
Neuronal Dynamics - Computational Neuroscience of Single Neurons
The activity of neurons in the brain and the code used by these neurons is described by mathematical neuron models at different levels of detail.
Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition
This course explains the mathematical and computational models that are used in the field of theoretical neuroscience to analyze the collective dynamics of thousands of interacting neurons.
Show more