Benchmarking cEEGrid and Solid Gel-Based Electrodes to Classify Inattentional Deafness in a Flight Simulator

Evaluation of a headphones-fitted EEG system for the recording of auditory evoked potentials and mental workload assessment

Advancements in portable neuroimaging technologies open up new opportunities to gain insight into the neural dynamics and cognitive processes underlying day-to-day behaviors. In this study, we evaluated the relevance of a headphone- mounted electroencephalogram (EEG) system for monitoring mental workload. The participants (N = 12) were instructed to pay attention to auditory alarms presented sporadically while performing the Multi-Attribute Task Battery (MATB) whose difficulty was staged across three conditions to manipulate mental workload. The P300 Event-Related Potentials (ERP) elicited by the presentation of auditory alarms were used as probes of attentional resources available. The amplitude and latency of P300 ERPs were compared across experimental conditions. Our findings indicate that the P300 ERP component can be captured using a headphone-mounted EEG system. Moreover, neural responses to alarm could be used to classify mental workload with high accuracy (over 80%) at a single-trial level. Our analyses indicated that the signal-to-noise ratio acquired by the sponge-based sensors remained stable throughout the recordings. These results highlight the potential of portable neuroimaging technology for the development of neuroassistive applications while underscoring the current limitations and challenges associated with the integration of EEG sensors in everyday-life wearable technologies. Overall, our study contributes to the growing body of research exploring the feasibility and validity of wearable neuroimaging technologies for the study of human cognition and behavior in real-world settings.

The Effect of Sensory Reweighting on Postural Control and Cortical Activity in Parkinson's Disease

Aims

Balance requires the cortical control of visual, somatosensory, and vestibular inputs. The aim of this cross-sectional study was to compare the contributions of each of these systems on postural control and cortical activity using a sensory reweighting approach between participants with Parkinson's disease (PD) and controls.

Methods

Ten participants with PD (age: 72 ± 9; 3 women; Hoehn & Yahr: 2 [1.5 - 2.50]) and 11 controls (age: 70 ± 3; 4 women) completed a sensory organization test in virtual reality (VR-SOT) while cortical activity was being recorded using electroencephalography (EEG). Conditions 1 to 3 were completed on a stable platform; conditions 4 to 6 on a foam. Conditions 1 and 4 were done with eyes open; conditions 2 and 5 in a darkened VR environment; and conditions 3 and 6 in a moving VR environment. Linear mixed models were used to evaluate changes in center of pressure (COP) displacement and EEG alpha and theta/beta ratio power between the two groups across the postural control conditions. Condition 1 was used as reference in all analyses.

Results

Participants with PD showed greater COP displacement than controls in the anteroposterior (AP) direction when relying on vestibular input (condition 5; p<0.0001). The mediolateral (ML) COP sway was greater in PD than in controls when relying on the somatosensory (condition 2; p = 0.03), visual (condition 4; p = 0.002), and vestibular (condition 5; p < 0.0001) systems. Participants with PD exhibited greater alpha power compared to controls when relying on visual input (condition 2; p = 0.003) and greater theta/beta ratio power when relying on somatosensory input (condition 4; p = 0.001).

Conclusions

PD affects reweighting of postural control, exemplified by greater COP displacement and increased cortical activity. Further research is needed to establish the temporal dynamics between cortical activity and COP displacement.

Smartphone-based ear-electroencephalography to study sound processing in everyday life

In everyday life, people differ in their sound perception and thus sound processing. Some people may be distracted by construction noise, while others do not even notice. With smartphone-based mobile ear-electroencephalography (ear-EEG), we can measure and quantify sound processing in everyday life by analysing presented sounds and also naturally occurring ones. Twenty-four participants completed four controlled conditions in the lab (1 h) and one condition in the office (3 h). All conditions used the same paired-click stimuli. In the lab, participants listened to click tones under four different instructions: no task towards the sounds, reading a newspaper article, listening to an audio article or counting a rare deviant sound. In the office recording, participants followed daily activities while they were sporadically presented with clicks, without any further instruction. In the beyond-the-lab condition, in addition to the presented sounds, environmental sounds were recorded as acoustic features (i.e., loudness, power spectral density and sounds onsets). We found task-dependent differences in the auditory event-related potentials (ERPs) to the presented click sounds in all lab conditions, which underline that neural processes related to auditory attention can be differentiated with ear-EEG. In the beyond-the-lab condition, we found ERPs comparable to some of the lab conditions. The N1 amplitude to the click sounds beyond the lab was dependent on the background noise, probably due to energetic masking. Contrary to our expectation, we did not find a clear ERP in response to the environmental sounds. Overall, we showed that smartphone-based ear-EEG can be used to study sound processing of well defined-stimuli in everyday life.

Predictions of task using neural modeling

Introduction

A well-designed brain-computer interface (BCI) can make accurate and reliable predictions of a user's state through the passive assessment of their brain activity; in turn, BCI can inform an adaptive system (such as artificial intelligence, or AI) to intelligently and optimally aid the user to maximize the human-machine team (HMT) performance. Various groupings of spectro-temporal neural features have shown to predict the same underlying cognitive state (e.g., workload) but vary in their accuracy to generalize across contexts, experimental manipulations, and beyond a single session. In our work we address an outstanding challenge in neuroergonomic research: we quantify if (how) identified neural features and a chosen modeling approach will generalize to various manipulations defined by the same underlying psychological construct, (multi)task cognitive workload.

Methods

To do this, we train and test 20 different support vector machine (SVM) models, each given a subset of neural features as recommended from previous research or matching the capabilities of commercial devices. We compute each model's accuracy to predict which (monitoring, communications, tracking) and how many (one, two, or three) task(s) were completed simultaneously. Additionally, we investigate machine learning model accuracy to predict task(s) within- vs. between-sessions, all at the individual-level.

Results

Our results indicate gamma activity across all recording locations consistently outperformed all other subsets from the full model. Our work demonstrates that modelers must consider multiple types of manipulations which may each influence a common underlying psychological construct.

Discussion

We offer a novel and practical modeling solution for system designers to predict task through brain activity and suggest next steps in expanding our framework to further contribute to research and development in the neuroergonomics community. Further, we quantified the cost in model accuracy should one choose to deploy our BCI approach using a mobile EEG-systems with fewer electrodes-a practical recommendation from our work.

Classification of Electrophysiological Signatures With Explainable Artificial Intelligence: The Case of Alarm Detection in Flight Simulator

Relevant sounds such as alarms are sometimes involuntarily ignored, a phenomenon called inattentional deafness. This phenomenon occurs under specific conditions including high workload (i.e., multitasking) and/or cognitive fatigue. In the context of aviation, such an error can have drastic consequences on flight safety. This study uses an oddball paradigm in which participants had to detect rare sounds in an ecological context of simulated flight. Cognitive fatigue and cognitive load were manipulated to trigger inattentional deafness, and brain activity was recorded via electroencephalography (EEG). Our results showed that alarm omission and alarm detection can be classified based on time-frequency analysis of brain activity. We reached a maximum accuracy of 76.4% when the algorithm was trained on all participants and a maximum of 90.5%, on one participant, when the algorithm was trained individually. This method can benefit from explainable artificial intelligence to develop efficient and understandable passive brain-computer interfaces, improve flight safety by detecting such attentional failures in real time, and give appropriate feedback to pilots, according to our ambitious goal, providing them with reliable and rich human/machine interactions.