EEG Correlates of Driving Performance

Abstract

Techniques for monitoring human performance traditionally rely on subjective responses and task-specific scoring, yet research suggests EEG could offer multiple performance metrics with high temporal resolution and accuracy that could be leveraged for human-computer interaction purposes. The objective of the presented work is to investigate which EEG responses correlate with task performance and evaluate whether combinations of these produce effective predictive models, facilitating further understanding of the psychological link to performance. A user study was conducted with 32 participants required to negotiate a driving course with the ambition of learning and improving ability on the course during an EEG recording session. EEG was filtered and post-processed to find Power Spectral Density (PSD) in alpha (α), beta (β), delta (δ), and theta (θ) frequency bands, as well as frontal alpha asymmetry (FAA). The initial laps were considered a baseline and an average performance improvement was calculated over the remaining laps in terms of percentage improvement in duration of track traversal. Results demonstrate Event Related Desynchronisation (ERD) with increased task performance in the alpha (p = .000), delta (p = .000), and theta (p = .000) bands, as well as evidence of a relationship between overall change in FAA and task efficiency. A full electrode analysis identifies δF 4 as the optimal for predicting collisions, with efficiency best predicted by a combination of β Oz and δF 4 .

Modulating Driver Alertness via Ambient Olfactory Stimulation: A Wearable Electroencephalography Study

“…Recently, a number of neuroergonomics studies have found that frontal alpha band power decreases are more pronounced with pro-active driving, rather than re-active driving, which further underlines the relevance of this frequency band in anticipatory attention [ 47 ]. Other studies found that alpha bands at fronto-central regions are correlated with driving task performance, and may be linked to individual differences in driving performance [ 48 ], whereas higher alpha power is significantly increased in cases where the cognitive system runs in an idle state [ 49 ], or with increased mind-wandering events during the driving task [ 50 ].…”

A Subject-Specific Attention Index Based on the Weighted Spectral Power

“…EEG signal in gamma band was also connected to attention and working memory and a significant increasement of the power in gamma band on parieto-occipital area was found while subjects are attending to visual rotating stimuli [17]. In addition, an increase of delta activity also appeared during performing mental tasks which required attention to internal processing [18], [19]. Among these frequency bands, the power of alpha, beta and theta bands is the most frequently utilized to measure attentional level.…”

“…The EEG rhythms are believed to relate to brain cognitive functions and are widely used in EEG analysis. Among them, alpha rhythm (8)(9)(10)(11)(12)(13), beta rhythm (13)(14)(15)(16)(17)(18)(19)(20)(21)(22), and theta rhythm (4-8 Hz) are the three most popular rhythm bands. Researchers from NASA developed a biocybernetic system based on evaluation of operator's engagement and sustained attention using indices computed by various spectral power.…”

Investigating multilevel cognitive processing within error-free and error-prone feedback conditions in executed and observed car driving

“…Indeed, an abundance of non-invasive brain research investigated numerous aspects of vehicle operation to the present day. Various groups reported neural correlates to or successful classification of, e.g., steering motion ( Simpson and Rafferty, 2022 ; Vecchiato et al, 2022 ), the driver’s mental state ( ElSherif et al, 2020 ), or the influences of distraction and fatigue ( Schneiders et al, 2020 ; Zeng et al, 2022 ) on the driving performance utilizing the electroencephalogram (EEG). Additionally, insights on discrete error-related brain activity occurring during the operation of a vehicle induced by wrong turns ( Zhang et al, 2015 ) or steering errors caused by the interface ( Garcia et al, 2017 ) emerged specifically in the context of brain-computer interfaces (BCIs).…”