Mobile Electroencephalography Research Articles

Reliable electrocortical dynamics of target-directed pass-kicks

AbstractFootball is one of the most played sports in the world and kicking with adequate accuracy increases the likelihood of winning a competition. Although studies with different target-directed movements underline the role of distinctive cortical activity on superior accuracy, little is known about cortical dynamics associated with kicking. Mobile electroencephalography is a popular tool to investigate cortical modulations during movement, however, inherent and artefact-related pitfalls may obscure the reliability of functional sources and their activity. The purpose of this study was therefore to describe consistent cortical dynamics underlying target-directed pass-kicks based on test–retest reliability estimates. Eleven participants performed a target-directed kicking task at two different sessions within one week. Electroencephalography was recorded using a 65-channel mobile system and behavioural data were collected including motion range, acceleration and accuracy performance. Functional sources were identified using independent component analysis and clustered in two steps with the components of first and subsequently both sessions. Reliability estimates of event-related spectral perturbations were computed pixel-wise for participants contributing with components of both sessions. The parieto-occipital and frontal clusters were reproducible for the same majority of the sample at both sessions. Their activity showed consistent alpha desyhronization and theta sychnronisation patterns with substantial reliability estimates revealing visual and attentional demands in different phases of kicking. The findings of our study reveal prominent cortical demands during the execution of a target-directed kick which may be considered in practical implementations and provide promising academic prospects in the comprehension and investigation of cortical activity associated with target-directed movements.

Fixation-related potentials during mobile map assisted navigation in the real world: The effect of landmark visualization style.

An often-proposed enhancement for mobile maps to aid assisted navigation is the presentation of landmark information, yet understanding of the manner in which they should be displayed is limited. In this study, we investigated whether the visualization of landmarks as 3D map symbols with either an abstract or realistic style influenced the subsequent processing of those landmarks during route navigation. We utilized a real-world mobile electroencephalography approach to this question by combining several tools developed to overcome the challenges typically encountered in real-world neuroscience research. We coregistered eye-movement and EEG recordings from 45 participants as they navigated through a real-world environment using a mobile map. Analyses of fixation event-related potentials revealed that the amplitude of the parietal P200 component was enhanced when participants fixated landmarks in the real world that were visualized on the mobile map in a realistic style, and that frontal P200 latencies were prolonged for landmarks depicted in either a realistic or abstract style compared with features of the environment that were not presented on the map, but only for the male participants. In contrast, we did not observe any significant effects of landmark visualization style on visual P1-N1 peaks or the parietal late positive component. Overall, the findings indicate that the cognitive matching process between landmarks seen in the environment and those previously seen on a map is facilitated by more realistic map display, while low-level perceptual processing of landmarks and recall of associated information are unaffected by map visualization style.

Mobile Electroencephalography Reveals Differences in Cortical Processing During Exercises With Lower and Higher Cognitive Demands in Preadolescent Children.

The aim of this study was to examine whether cortical activity changes during exercise with increasing cognitive demands in preadolescent children. Twenty healthy children (8.75 [0.91]y) performed one movement game, which was conducted with lower and higher cognitive demands. During a baseline measurement and both exercise conditions, cortical activity was recorded using a 64-channel electroencephalographic system, and heart rate was assessed. Ratings of perceived excertion and perceived cognitive engagement were examined after each condition. To analyze power spectral density in the theta, alpha-1, and alpha-2 frequency bands, an adaptive mixture independent component analysis was used to determine the spatiotemporal sources of cortical activity, and brain components were clustered to identify spatial clusters. One-way repeated-measures analyses of variance revealed significant main effects for condition on theta in the prefrontal cluster, on alpha-1 in the prefrontal, central, bilateral motor, bilateral parieto-occipital, and occipital clusters, and on alpha-2 in the left motor, central, and left parieto-occipital clusters. Compared with the lower cognitive demand exercise, cortical activity was significantly higher in theta power in the prefrontal cluster and in alpha-1 power in the occipital cluster during the higher cognitive demand exercise. The present study shows that exercise complexity seems to influence cortical processing as it increased with increasing cognitive demands.

Assessing attentional task-related electroencephalogram signal variations by using mobile electroencephalogram technology: An experimental study.

A better understanding of the network responses of cortical activities during rest and cognitive tasks is necessary. Therefore, in this study, we aimed to evaluate cerebral activities during attentional tasks by using mobile electroencephalography, identifying the types of attentional components and brain waves. In this experimental study, we enrolled 12 healthy young adults. The attentional tasks comprised parts A and B of the Trail-Making Test (TMT). Nineteen electroencephalography electrodes were placed over various brain regions. The Wilcoxon signed-rank test was used to examine the differences in power levels between the rest and TMT conditions. During TMT part A, the electroencephalography power level of the delta waves was significantly higher in the right frontal, left central, left occipital, left inferior frontal, right mid-temporal, right posterior temporal, and middle parietal areas (P < .05) than those during the resting state; that of the alpha waves was significantly lower in the left posterior temporal area (P = .006); and that of the high gamma waves was significantly lower in the left parietal (P = .05) and left occipital (P = .002) areas. During TMT part B, the electroencephalography power level of the beta waves was significantly higher in the right frontal area (P = .041) than that during the resting state, and that of the low gamma waves was significantly higher in the left frontal pole, right frontal, and right inferior frontal areas (P < .05). During the focused attentional task, the power level of the delta waves increased and that of the alpha waves decreased, and during the alternating attentional task, those of both the beta and gamma waves increased. The delta waves were related to the whole brain, the alpha and high gamma waves to the left posterior lobe, and the beta and low gamma waves to both frontal lobes. These findings contribute to the basic knowledge necessary to develop new attentional assessment methods for clinical situations.

Introducing a New Mobile Electroencephalography System and Evaluating Its Quality in Comparison to Clinical Electroencephalography.

Electroencephalography (EEG) is a crucial tool in cognitive neuroscience, enabling the study of neurophysiological function by measuring the brain's electrical activity. Its applications include perception, learning, memory, language, decision making and neural network mapping. Recently, interest has surged in extending EEG measurements to domestic environments. However, the high costs associated with traditional laboratory EEG systems have hindered accessibility for many individuals and researchers in education, research, and medicine. To tackle this, a mobile-EEG device named "DreamMachine" was developed. A more affordable alternative to both lab-based EEG systems and existing mobile-EEG devices. This system boasts 24 channels, 24-bit resolution, up to 6 h of battery life, portability, and a low price. Our open-source and open-hardware approach empowers cognitive neuroscience, especially in education, learning, and research, opening doors to more accessibility. This paper introduces the DreamMachine's design and compares it with the lab-based EEG system "asalabTM" in an eyes-open and eyes-closed experiment. The Alpha band exhibited higher power in the power spectrum during eyes-closed conditions, whereas the eyes-open condition showed increased power specifically within the Delta frequency range. Our analysis confirms that the DreamMachine accurately records brain activity, meeting the necessary standards when compared to the asalabTM system.

Adhesive Wearable Sensors for Electroencephalography from Hairy Scalp.

Electroencephalography has garnered interest for applications in mobile healthcare, human-machine interfaces, and IoT. Conventional electroencephalography relies on wet and dry electrodes. Despite favourable interface impedance of wet electrodes and skin, the application of a large amount of gel at their interface with skin limits the electroencephalography spatial resolution, increases the risk of shorting between electrodes and makes them unsuited for long-term mobile recording. In contrast, dry electrodes are better suited for long-term recordings but susceptible to motion artifacts. In addition, both wet and dry electrodes are non-adhesive to the hairy scalp and mechanical support, or chemical adhesives are used to hold them in place. Here, we report a conical microstructure array (CMSA) based sensor made of carbon nanotube-polydimethylsiloxane composite. The CMSA sensor is fabricated using our innovative, cost-effective, and scalable method of viscosity-controlled dip-pull process. The sensor adheres to the hairy scalp by generating negative pressure in its conical microstructures when it is pressed against scalp. Aided by the application of a trace amount of gel, CMSA sensor establishes good electrical contact with the skin, enabling its applications in mobile electroencephalography over extended periods. Notably, the signal quality of CMSA sensors is comparable to that of medical-grade wet gel electrodes. This article is protected by copyright. All rights reserved.

Day-to-Day Individual Alpha Frequency Variability Measured by a Mobile EEG Device Relates to Anxiety.

The individual alpha frequency (IAF) has previously been identified as a unique neural signature within the 8-12 Hz alpha frequency band. However, the day-to-day variability of this feature is unknown. To accomplish this, healthy participants recorded their own brain activity daily at home using the Muse 2 headband, a low-cost consumer-grade mobile electroencephalography (EEG) device. Resting-state recordings of all participants using a high-density (HD) EEG were also collected in lab before and after the at-home data collection period. We found that the IAF extracted from the Muse 2 was comparable to that of location-matched HD-EEG electrodes. No significant difference was found between these IAF values before and after the at-home recording period for the HD-EEG device. Similarly, there was also no statistically significant difference between the beginning and end of the at-home recording period for the Muse 2 headband over one month. Despite the group-level stability of IAF, the individual-level day-to-day IAF variability carried mental health-relevant information: exploratory analyses revealed a relationship between IAF day-to-day variability and trait anxiety. We also noted that the IAF systematically varied across the scalp and although the Muse 2 electrodes do not cover the occipital lobe where alpha oscillations were the strongest, IAFs measured in the temporal lobe and occipital lobe were strongly correlated. Altogether, these results show that mobile EEG devices are useful for studying IAF variability. The relationship between day-to-day variability of region-specific IAF and the dynamics of psychiatric symptoms, particularly anxiety, should be further investigated.

Walk the plank! Using mobile electroencephalography to investigate emotional lateralization of immersive fear in virtual reality.

Most studies on emotion processing induce emotions through images or films. However, this method lacks ecological validity, limiting generalization to real-life emotion processing. More realistic paradigms using virtual reality (VR) may be better suited to investigate authentic emotional states and their neuronal correlates. This pre-registered study examines the neuronal underpinnings of naturalistic fear, measured using mobile electroencephalography (EEG). Seventy-five healthy participants walked across a virtual plank which extended from the side of a skyscraper-either 80 storeys up (the negative condition) or at street level (the neutral condition). Subjective ratings showed that the negative condition induced feelings of fear. Following the VR experience, participants passively viewed negative and neutral images from the international affective picture system (IAPS) outside of VR. We compared frontal alpha asymmetry between the plank and IAPS task and across valence of the conditions. Asymmetry indices in the plank task revealed greater right-hemispheric lateralization during the negative VR condition, relative to the neutral VR condition and to IAPS viewing. Within the IAPS task, no significant asymmetries were detected. In summary, our findings indicate that immersive technologies such as VR can advance emotion research by providing more ecologically valid ways to induce emotion.

How low can you go? Measuring human event-related brain potentials from a two-channel EEG system

Over the past ten years, there has been a rapid increase in the availability and use of mobile electroencephalography (mEEG) in research. Indeed, researchers using mEEG have recorded EEG and event-related brain potentials in a wide range of environments - for example, while walking (Debener et al., 2012), riding a bike (Scanlon et al., 2020), or even in a shopping mall (Krigolson et al., 2021). However, given that low-cost, ease-of-use, and setup speed provide the primary advantages of an mEEG system over large array traditional EEG systems, an important and unresolved question is just how many electrodes does an mEEG system need to collect research-quality EEG data? Here, we tested whether or not a two-channel forehead-mounted mEEG system – the “Patch” – could measure event-related brain potentials within their established amplitude and latency characteristics (Luck, 2014). In the present study, participants performed a visual oddball task while we recorded EEG data from the Patch. Our results demonstrated that we could capture and quantify the N200 and P300 event-related brain potential components using a minimal electrode array forehead-mounted EEG system. Our data further support the idea that mEEG can be used for quick and rapid EEG-based assessments, such as measuring the impact of concussions on the sports field (Fickling et al., 2021) or assessing the impact of stroke severity in a hospital (Wilkinson et al., 2020).

Investigating the attentional focus to workplace-related soundscapes in a complex audio-visual-motor task using EEG.

In demanding work situations (e.g., during a surgery), the processing of complex soundscapes varies over time and can be a burden for medical personnel. Here we study, using mobile electroencephalography (EEG), how humans process workplace-related soundscapes while performing a complex audio-visual-motor task (3D Tetris). Specifically, we wanted to know how the attentional focus changes the processing of the soundscape as a whole. Participants played a game of 3D Tetris in which they had to use both hands to control falling blocks. At the same time, participants listened to a complex soundscape, similar to what is found in an operating room (i.e., the sound of machinery, people talking in the background, alarm sounds, and instructions). In this within-subject design, participants had to react to instructions (e.g., "place the next block in the upper left corner") and to sounds depending on the experimental condition, either to a specific alarm sound originating from a fixed location or to a beep sound that originated from varying locations. Attention to the alarm reflected a narrow attentional focus, as it was easy to detect and most of the soundscape could be ignored. Attention to the beep reflected a wide attentional focus, as it required the participants to monitor multiple different sound streams. Results show the robustness of the N1 and P3 event related potential response during this dynamic task with a complex auditory soundscape. Furthermore, we used temporal response functions to study auditory processing to the whole soundscape. This work is a step toward studying workplace-related sound processing in the operating room using mobile EEG.

ICanClean Improves Independent Component Analysis of Mobile Brain Imaging with EEG.

Motion artifacts hinder source-level analysis of mobile electroencephalography (EEG) data using independent component analysis (ICA). iCanClean is a novel cleaning algorithm that uses reference noise recordings to remove noisy EEG subspaces, but it has not been formally tested in a parameter sweep. The goal of this study was to test iCanClean’s ability to improve the ICA decomposition of EEG data corrupted by walking motion artifacts. Our primary objective was to determine optimal settings and performance in a parameter sweep (varying the window length and r2 cleaning aggressiveness). High-density EEG was recorded with 120 + 120 (dual-layer) EEG electrodes in young adults, high-functioning older adults, and low-functioning older adults. EEG data were decomposed by ICA after basic preprocessing and iCanClean. Components well-localized as dipoles (residual variance < 15%) and with high brain probability (ICLabel > 50%) were marked as ‘good’. We determined iCanClean’s optimal window length and cleaning aggressiveness to be 4-s and r2 = 0.65 for our data. At these settings, iCanClean improved the average number of good components from 8.4 to 13.2 (+57%). Good performance could be maintained with reduced sets of noise channels (12.7, 12.2, and 12.0 good components for 64, 32, and 16 noise channels, respectively). Overall, iCanClean shows promise as an effective method to clean mobile EEG data.

Explore your brain: A randomized controlled trial into the effectiveness of a growth mindset intervention with psychosocial and psychophysiological components.

Although past research demonstrated growth mindset interventions to improve school outcomes, effects were small. This may be due to the theoretical nature of psychosocial techniques (e.g., reading about brain plasticity), which may not be optimally convincing for students. To address this issue and improve effectiveness, we developed a growth mindset intervention, which combined psychosocial and psychophysiological components. The latter adds a convincing experience of influencing one's own brain activity, using mobile electroencephalography (EEG) neurofeedback, emphasizing the controllable and malleable nature of one's brain. In this randomized controlled trial (RCT), twenty high-school classes (N=439) were randomized to either the active control condition (no mindset messaging) or our newly developed growth mindset intervention condition (4 × 50 min). School outcomes (pre, post, 1-year follow-up) were analysed with Linear Mixed Models (LMM: variable-oriented) and Latent Transition Analysis (LTA: person-oriented). LMM: students in the growth mindset intervention reported increased growth mindset directly after the intervention (post, d=.38) and at 1-year follow-up (d=.25) and demonstrated a protective effect against deterioration of math grades at 1-year follow-up (d=.36), compared to controls. LTA: we identified three mindset profiles (Fixed, Growth competitive, Growth non-competitive), with more frequent transitions from fixed to one of the growth mindset profiles at 1-year follow-up for students in the growth mindset intervention compared to controls (OR 2.58-2.68). Compared to previous studies, we found relatively large effects of our intervention on growth mindset and math grades, which may be attributable to synergetic effects of psychosocial and psychophysiological (neurofeedback) components. The person-oriented approach demonstrated more holistic effects, involving multiple motivational constructs.

Context effects on object recognition in real-world environments: A study protocol.

Background: The environments that we live in impact on our ability to recognise objects, with recognition being facilitated when objects appear in expected locations (congruent) compared to unexpected locations (incongruent). However, these findings are based on experiments where the object is isolated from its environment. Moreover, it is not clear which components of the recognition process are impacted by the environment. In this experiment, we seek to examine the impact real world environments have on object recognition. Specifically, we will use mobile electroencephalography (mEEG) and augmented reality (AR) to investigate how the visual and semantic processing aspects of object recognition are changed by the environment. Methods: We will use AR to place congruent and incongruent virtual objects around indoor and outdoor environments. During the experiment a total of 34 participants will walk around the environments and find these objects while we record their eye movements and neural signals. We will perform two primary analyses. First, we will analyse the event-related potential (ERP) data using paired samples t-tests in the N300/400 time windows in an attempt to replicate congruency effects on the N300/400. Second, we will use representational similarity analysis (RSA) and computational models of vision and semantics to determine how visual and semantic processes are changed by congruency. Conclusions: Based on previous literature, we hypothesise that scene-object congruence would facilitate object recognition. For ERPs, we predict a congruency effect in the N300/N400, and for RSA we predict that higher level visual and semantic information will be represented earlier for congruent scenes than incongruent scenes. By collecting mEEG data while participants are exploring a real-world environment, we will be able to determine the impact of a natural context on object recognition, and the different processing stages of object recognition.

Applying Deep Learning on a Few EEG Electrodes during Resting State Reveals Depressive States. A Data Driven Study.

The growing number of depressive people and the overload in primary care services make it necessary to identify depressive states with easily accessible biomarkers such as mobile electroencephalography (EEG). Some studies have addressed this issue by collecting and analyzing EEG resting state in a search of appropriate features and classification methods. Traditionally, EEG resting state classification methods for depression were mainly based on linear or a combination of linear and non-linear features. We hypothesize that participants with ongoing depressive states differ from controls in complex patterns of brain dynamics that can be captured in EEG resting state data, using only nonlinear measures on a few electrodes, making it possible to develop cheap and wearable devices that could be even monitored through smartphones. To validate such a perspective, a resting-state EEG study was conducted with 50 participants, half with depressive state (DEP) and half controls (CTL). A data-driven approach was applied to select the most appropriate time window and electrodes for the EEG analyses, as suggested by Giacometti⁠, as well as the most efficient nonlinear features and classifiers, to distinguish between CTL and DEP participants. Nonlinear features showing temporo-spatial and spectral complexity were selected. The results confirmed that computing nonlinear features from a few selected electrodes in a 15 s time window are sufficient to classify DEP and CTL participants accurately. Finally, after training and testing internally the classifier, the trained machine was applied to EEG resting state data (CTL and DEP) from a publicly available database, validating the capacity of generalization of the classifier with data from different equipment, population, and environment obtaining an accuracy near 100%.

A mobile EEG study on the psychophysiological effects of walking and crowding in indoor and outdoor urban environments

Environmental psychologists have established multiple psychological benefits of interaction with natural, compared to urban, environments on emotion, cognition, and attention. Yet, given the increasing urbanisation worldwide, it is equally important to understand how differences within different urban environments influence human psychological experience. We developed a laboratory experiment to examine the psychophysiological effects of the physical (outdoor or indoor) and social (crowded versus uncrowded) environment in healthy young adults, and to validate the use of mobile electroencephalography (EEG) and electrodermal activity (EDA) measurements during active walking. Participants (N = 42) were randomly assigned into a walking or a standing group, and watched six 1-min walk-through videos of green, urban indoor and urban outdoor environments, depicting high or low levels of social density. Self-reported emotional states show that green spaces is perceived as more calm and positive, and reduce attentional demands. Further, the outdoor urban space is perceived more positively than the indoor environment. These findings are consistent with earlier studies on the psychological benefits of nature and confirm the effectiveness of our paradigm and stimuli. In addition, we hypothesised that even short-term exposure to crowded scenes would have negative psychological effects. We found that crowded scenes evoked higher self-reported arousal, more negative self-reported valence, and recruited more cognitive and attentional resources. However, in walking participants, they evoked higher frontal alpha asymmetry, suggesting more positive affective responses. Furthermore, we found that using recent signal-processing methods, the EEG data produced a comparable signal-to-noise ratio between walking and standing, and that despite differences between walking and standing, skin-conductance also captured effectively psychophysiological responses to stimuli. These results suggest that emotional responses to visually presented stimuli can be measured effectively using mobile EEG and EDA in ambulatory settings, and that there is complex interaction between active walking, the social density of urban spaces, and direct and indirect affective responses to such environments.

Capturing Cognitive Events Embedded in the Real World Using Mobile Electroencephalography and Eye-Tracking

The study of cognitive processes underlying natural behaviors implies departing from computerized paradigms and artificial experimental probes. The present study aims to assess the feasibility of capturing neural markers (P300 ERPs) of cognitive processes evoked in response to the identification of task-relevant objects embedded in a real-world environment. To this end, EEG and eye-tracking data were recorded while participants attended stimuli presented on a tablet and while they searched for books in a library. Initial analyses of the library data revealed that P300-like features shifted in time. A Dynamic Time Warping analysis confirmed the presence of P300 ERP in the library condition. Library data were then lag-corrected based on cross-correlation coefficients. Together, these approaches uncovered P300 ERP responses in the library recordings. These findings highlight the relevance of scalable experimental designs, joint brain and body recordings, and template-matching analyses to capture cognitive events during natural behaviors.

Mobile electroencephalography captures differences of walking over even and uneven terrain but not of single and dual-task gait.

Walking on natural terrain while performing a dual-task, such as typing on a smartphone is a common behavior. Since dual-tasking and terrain change gait characteristics, it is of interest to understand how altered gait is reflected by changes in gait-associated neural signatures. A study was performed with 64-channel electroencephalography (EEG) of healthy volunteers, which was recorded while they walked over uneven and even terrain outdoors with and without performing a concurrent task (self-paced button pressing with both thumbs). Data from n = 19 participants (M = 24 years, 13 females) were analyzed regarding gait-phase related power modulations (GPM) and gait performance (stride time and stride time-variability). GPMs changed significantly with terrain, but not with the task. Descriptively, a greater beta power decrease following right-heel strikes was observed on uneven compared to even terrain. No evidence of an interaction was observed. Beta band power reduction following the initial contact of the right foot was more pronounced on uneven than on even terrain. Stride times were longer on uneven compared to even terrain and during dual- compared to single-task gait, but no significant interaction was observed. Stride time variability increased on uneven terrain compared to even terrain but not during single- compared to dual-tasking. The results reflect that as the terrain difficulty increases, the strides become slower and more irregular, whereas a secondary task slows stride duration only. Mobile EEG captures GPM differences linked to terrain changes, suggesting that the altered gait control demands and associated cortical processes can be identified. This and further studies may help to lay the foundation for protocols assessing the cognitive demand of natural gait on the motor system.

Brain Activity and Aggressive Behavior of Online Gamers

Research has shown mixed results in studies of online gaming and its effects, including aggression. The purpose of the current study is to examine the effects of a violent and non-violent game on aggression levels and brain activity. Forty-seven participants completed online questionnaires during pre- and post-gaming. During gaming, participants were randomly assigned to play either an action survival game or puzzle game. A mobile electroencephalography (EEG) recording device was used to record brain activity. Questionnaire results show significant positive correlations between problematic online gaming and aggression. The sub-dimensions of immersion, overuse and physical aggression showed a tendency to increase in the violent condition, while social isolation decreased in the non-violent condition. EEG data revealed some distinctions between the conditions. Frontal asymmetry analyses showed withdrawal-related activation throughout the experiment. Findings obtained suggest a behavioral and neurological distinction in playing different types of games.

Cortical responses to sport-specific stimuli in a standing stop signal task

Executive functions (EF) and especially inhibition have been shown to play an essential role in team sports. However, the underlying cortical mechanisms of EF in real game situations are barely understood, since previous studies predominantly used tests with simplified stimuli in seated positions. Consequently, the aim of the current study was to explore cortical responses to sport-specific stimuli in a standing stop signal task (SST). Cortical activity was investigated in 17 adult amateur soccer players using mobile electroencephalography (EEG) in a standard- (Std) and a sport-specific (Sp) SST. Whereas mean reaction times were significantly longer in the Sp-SST, stop error-rates demonstrated no significant difference. Further, analysis of event-related spectral perturbation (ERSP) in a frontal cluster also showed no significant differences between Std- and Sp-SST for go and stop conditions. Thus, sport-specific stimuli in a standing SST may exhibit similar inhibitory cortical processes as the standard SST, although requiring longer processing times with regards to the more complex visual stimulation. Future studies are needed to further develop realistic paradigms for evaluating sport-specific cognitive performance.

P.105 Using mobile electroencephalography for rapid detection of mild cognitive impairment

Background: Mild cognitive impairment (MCI) is a concern for our aging population as it can be a pre-cursor to dementia. However, the diagnosis of MCI can be quite problematic and can come long after initial onset. Here, we sought to use a new technology we have previously validated for research – mobile electroencephalography (mEEG) – to measure brain function to see if we could rapidly detect differences in brain activity between people with and without MCI. Methods: Participants (60: mean age 65) were recruited for a control (30) and an MCI group (30). All participants were screened for MCI using standard RBANS and the MOCA assessments. Participants completed a standard n-Back assessment of working memory while mEEG data was recorded. A key feature here is that we used mEEG technology thus application of the device and the n-Back test was completed in under 10 minutes for each participant. Results: Our key finding is that we observed increased frontal mEEG theta power (brain oscillations between 4 and 7 Hz) for MCI participants relative to controls (p &lt; 0.001). Conclusions: Importantly, our work demonstrates a potential novel rapid brain-based assessment for MCI that would afford earlier detection of disease onset.