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 .

Conventional performance metrics fail to offer highresolution evaluation of learning and memory during training tasks; the P300 component of the event-related potential (ERP) is a promising tool for enhancing the assessment of training quality in virtual environments, but this technique is yet to be investigated. A driver training simulator and scenario were developed to explore the capability of the P300 for this purpose. A user study was conducted with 32 participants divided into 2 groups objectively determined by driving performance scores, thus enabling observations of the P300 response to be equated to varying levels of learning and memory. Participant electroencephalogram (EEG) data was recorded during the procedure, which was post-processed to filter and extract ERPs to capture neural responses to specific events in the virtual training scenario. These were combined to produce a result for each participant, which was then grand averaged to create an overall ERP for each group. Across the eight electrode sites statistically significant differences were found between the grand average waveforms of the two groups, with high memory retention producing significantly greater peak to peak amplitude (U = 9.00, p = .045), peak latency (U = 0.00, p < .001), and positive area (U = 13.00, p = .05) of the waveform than low memory retention. The evidenced relationship between the P300 response and working memory in this context suggests it has the potential for monitoring learning and memory in stimulus-driven virtual training systems.

Haptic technologies have the capacity to enhance motor learning, potentially improving the safety and quality of operating performance in a variety of applications, yet there is limited research evaluating implementation of these devices in driver training environments. A driving simulator and training scenario were developed to assess the quality of motor learning produced with wrist-attached vibrotactile haptic motors for additional reinforcement feedback. User studies were conducted with 36 participants split into 2 groups based on feedback modality. Throughout the simulation vehicle interactions with the course were recorded, enabling comparisons of pre and post-training performance between the groups to evaluate short-term retention of the steering motor skill. Statistically significant differences were found between the two groups for vehicle position safety violations (U = 78.50, P = 0.008) where the visualhaptic group improved significantly more than the visual group. The Raw NASA-TLX (RTLX) was completed by participants to examine the cognitive effect of the additional modality, where the visual-haptic group reported greater levels of workload (U = 90.50, P = 0.039). In conclusion, reinforcement vibrotactile haptics can enhance short-term retention of motor learning with a positive effect on the safety and quality of posttraining behaviour, which is likely a result of increased demand and stimulation encouraging the adaptation of sensorimotor transformations.