Electroencephalography Data Research Articles

Cognitive impairment in multiple sclerosis: Utility of electroencephalography.

to evaluate associations between neurocognitive impairment and electroencephalography (EEG) data in Multiple Sclerosis (MS). patients aged between 18 and 65 years, diagnosed with MS accordingly to the McDonald 2017 criteria and who were in remission for at least one month were included. Cognitive functions were evaluated by validated neuropsychological tests for Tunisian population. Electroencephalography data of each patient were analysed, Grand Total EEG (GTE) score was calculated and we evaluated their statistical links with cognitive impairment. Thirty five patients were included. Slower background activity was associated with presence of: reduced information processing speed (IPS) (p=0,03), verbal memory impairment (p=0,04) and executive dysfunction (p=0,016). The score 3 of GTE (reactivity of background activity) was associated with reduced IPS (p=0,007) and executive dysfunction (p=0,014). We found a positive correlation between background activity and Tunisian Verbal Test (TVLT) (ρ =0,46; p=0,005) and Symbol Digit Modalities Test (SDMT) (ρ =0,35; p=0,03). Sensitivity of GTE score was 68,4% for executive dysfunction (cut-off=2,5) and 66,7% for reduced IPS (cut-off=2,5). Our results have shown utility of EEG in detecting cortical involvement and its correlation with cognitive impairment in MS patients. EEG could be a tool for monitoring cortical involvement during MS and predict cognitive impairment.

Measurement and Analysis of Cognitive Load Associated with Moving Object Classification in Underwater Environments

Visual analysis in field science experiments often involves classifying objects on experimental images and videos. In this context, developing a reliable and independently validated estimate of mental workload during object classification can enable cognitively responsive task allocation. The goal of this study is to quantify the cognitive load perceived by humans from electroencephalography (EEG) data during an underwater object classification task that was inspired from citizen science studies. During the task, participants were asked to identify one of three possible invasive fish species in short videos of a virtual underwater environment. The virtual environment was modeled to vary fish behavior and environmental factors that are known to be critical in classification. A contextually-relevant secondary task was designed to provide independent validation of cognitive load measures. Several established measures of cognitive load were compared across different weightings on the scalp positions, and the measure that strongly associated with reaction time and a secondary task accuracy was selected for further analysis. Our results show that cognitive load calculated using the difference in power of alpha frequencies best correlates with reaction time and secondary task accuracy. When fit to the environmental factors, cognitive load calculated using this approach was high when the environment was turbid and the fish moved at high speeds. Results from this study have applications in cognitively-responsive human–computer interaction and in developing shared control strategies in human–robot interaction.

EEG-based detection of modality-specific visual and auditory sensory processing

Objective. A passive brain–computer interface (pBCI) is a system that enhances a human–machine interaction by monitoring the mental state of the user and, based on this implicit information, making appropriate modifications to the interaction. Key to the development of such a system is the ability to reliably detect the mental state of interest via neural signals. Many different mental states have been investigated, including fatigue, attention and various emotions, however one of the most commonly studied states is mental workload, i.e. the amount of attentional resources required to perform a task. The emphasis of mental workload studies to date has been almost exclusively on detecting and predicting the ‘level’ of cognitive resources required (e.g. high vs. low), but we argue that having information regarding the specific ‘type’ of resources (e.g. visual or auditory) would allow the pBCI to apply more suitable adaption techniques than would be possible knowing just the overall workload level. Approach. 15 participants performed carefully designed visual and auditory tasks while electroencephalography (EEG) data was recorded. The tasks were designed to be as similar as possible to one another except for the type of attentional resources required. The tasks were performed at two different levels of demand. Using traditional machine learning algorithms, we investigated, firstly, if EEG can be used to distinguish between auditory and visual processing tasks and, secondly, what effect level of sensory processing demand has on the ability to distinguish between auditory and visual processing tasks. Main results. The results show that at the high level of demand, the auditory vs. visual processing tasks could be distinguished with an accuracy of 77.1% on average. However, in the low demand condition in this experiment, the tasks were not classified with an accuracy exceeding chance. Significance. These results support the feasibility of developing a pBCI for detecting not only the level, but also the type, of attentional resources being required of the user at a given time. Further research is required to determine if there is a threshold of demand under which the type of sensory processing cannot be detected, but even if that is the case, these results are still promising since it is the high end of demand that is of most concern in safety critical scenarios. Such a BCI could help improve safety in high risk occupations by initiating the most effective and efficient possible adaptation strategies when high workload conditions are detected.

Optimization of phase prediction for brain-state dependent stimulation: a grid-search approach

Objective. Sources of heterogeneity in non-invasive brain stimulation literature can be numerous, with underlying brain states and protocol differences at the top of the list. Yet, incoherent results from brain-state-dependent stimulation experiments suggest that there are further factors adding to the variance. Hypothesizing that different signal processing pipelines might be partly responsible for heterogeneity; we investigated their effects on brain-state forecasting approaches. Approach. A grid-search was used to determine the fastest and most-accurate combination of preprocessing parameters and phase-forecasting algorithms. The grid-search was applied on a synthetic dataset and validated on electroencephalographic (EEG) data from a healthy (n = 18) and stroke (n = 31) cohort. Main results. Differences in processing pipelines led to different results; the grid-search chosen pipelines significantly increased the accuracy of published forecasting methods. The accuracy achieved in healthy was comparably high in stroke patients. Significance. This systematic offline analysis highlights the importance of the specific EEG processing and forecasting pipelines used for online state-dependent setups where precision in phase prediction is critical. Moreover, successful results in the stroke cohort pave the way to test state-dependent interventional treatment approaches.

SSAVE: A tool for analysis and visualization of sleep periods using electroencephalography data.

Human sleep architecture is structured with repeated episodes of rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep. An overnight sleep study facilitates identification of macro and micro changes in the pattern and duration of sleep stages associated with sleep disorders and other aspects of human mental and physical health. Overnight sleep studies record, in addition to electroencephalography (EEG) and other electro-physiological signals, a sequence of sleep-stage annotations. SSAVE, introduced here, is open-source software that takes sleep-stage annotations and EEG signals as input, identifies and characterizes periods of NREM and REM sleep, and produces a hypnogram and its time-matched EEG spectrogram. SSAVE fills an important gap for the rapidly growing field of sleep medicine by providing an easy-to-use tool for sleep-period identification and visualization. SSAVE can be used as a Python package, a desktop standalone tool or through a web portal. All versions of the SSAVE tool can be found on: https://manticore.niehs.nih.gov/ssave.

Effects of reading contextualized physics problems among men and women: A psychophysiological approach

To counteract declining interest in science, contextualizing course material has been suggested, despite little evidence supporting this strategy. We assessed how reading physics problems in different contexts–none, technical, or humanistic–impacted performance and implicit cognitive and affective situational interest (SI) among undergraduate men and women (n = 60). We hypothesized that contextualized problems would increase cognitive SI, boosting performance. We also investigated existing hypotheses that this influence would be stronger when contexts matched stereotypical gender interests. Pupillometric and electroencephalographic data served to indicate cognitive SI, while electrodermal activity (EDA) and valence were measures of affective SI. Significantly higher valence was observed in decontextualized than humanistic problems (p = 0.003) specifically among men (p < 0.001). Greater EDA (p = 0.019) and decontextualized problems (p < 0.001) yielded greater performance than contextualized problems for all participants. Results emphasize the importance of affective SI and of avoiding gender biases in curricular development. This study encourages caution if implementing contextualization.

Type of Trauma Exposure Impacts Neural Reactivity to Errors

Abstract: Studies suggest that individuals with a history of trauma exposure display abnormal reactivity to threat, though the pattern of findings across prior studies has been inconsistent. At least two factors likely contribute to previous discrepant findings: (1) the type of index trauma event and (2) the type of threat paradigm. Accordingly, the current study aimed to examine the impact of trauma type on a specific psychophysiological index of threat sensitivity – error negativity (Ne), also described as error-related negativity (ERN). Young adults were classified into three groups: lifetime history of interpersonal trauma (i.e., sexual assault, physical assault, or immediate family violence; n = 30), lifetime history of a non-interpersonal trauma (e.g., accidents, natural disasters; n = 30), or no lifetime history of trauma ( n = 64). All participants completed a well-validated flanker task designed to elicit the Ne/ERN during continuous electroencephalographic (EEG) data collection. Results indicated that individuals with non-interpersonal trauma exposure displayed reduced Ne/ERN amplitude compared with the other two groups (who did not differ from each other). Broadly, these findings highlight the importance of trauma type and theory suggesting different forms of trauma may result in different neurobiological profiles. These findings also add to a growing literature indicating that non-interpersonal traumas may be uniquely associated with blunted threat sensitivity and deficiencies in self-monitoring.

Position representations of moving objects align with real-time position in the early visual response.

When interacting with the dynamic world, the brain receives outdated sensory information, due to the time required for neural transmission and processing. In motion perception, the brain may overcome these fundamental delays through predictively encoding the position of moving objects using information from their past trajectories. In the present study, we evaluated this proposition using multivariate analysis of high temporal resolution electroencephalographic data. We tracked neural position representations of moving objects at different stages of visual processing, relative to the real-time position of the object. During early stimulus-evoked activity, position representations of moving objects were activated substantially earlier than the equivalent activity evoked by unpredictable flashes, aligning the earliest representations of moving stimuli with their real-time positions. These findings indicate that the predictability of straight trajectories enables full compensation for the neural delays accumulated early in stimulus processing, but that delays still accumulate across later stages of cortical processing.

Changes of adrenocorticotropic hormone rhythm and cortisol circadian rhythm in patients with depression complicated with anxiety and their effects on the psychological state of patients.

Objective: This work was to explore the rhythm of adrenocorticotropic hormone (ACTH) and cortisol in patients with depression and anxiety and their effects on mental state. In this work, with depression complicated with anxiety patients as the A-MDD group (n = 21), and depression without anxiety symptoms as the NA-MDD group (n = 21). Firstly, data features were extracted according to the electroencephalo-graph (EEG) data of different patients, and a DR model was constructed for diagnosis. The Hamilton Depression Scale 24 (HAMD-24) was employed to evaluate the severity, and the ACTH and cortisol levels were detected and compared for patients in the A-MDD group and NA-MDD group. In addition, the psychological status of the patients was assessed using the Toronto Alexithymia Scale (TAS). As a result, the AI-based DR model showed a high recognition accuracy for depression. The HAMD-24 score in the A-MDD group (31.81 ± 5.39 points) was statistically higher than the score in the NA-MDD group (25.25 ± 5.02 points) (P < 0.05). No visible difference was found in ACTH levels of patients in different groups (P > 0.05). The incidence of cortisol rhythm disorder (CRD) in the A-MDD group was much higher (P < 0.05). The differences in TAS scores between the two groups were significantly statistically significant (P < 0.01). In conclusion, the AI-based DR Model achieves a more accurate identification of depression; depression with or without anxiety has different effects on the mental state of patients. CRD may be one of the biological markers of depression combined with anxiety.

Spike patterns surrounding sleep and seizures localize the seizure-onset zone in focal epilepsy.

Interictal spikes help localize seizure generators as part of surgical planning for drug-resistant epilepsy. However, there are often multiple spike populations whose frequencies change over time, influenced by brain state. Understanding state changes in spike rates will improve our ability to use spikes for surgical planning. Our goal was to determine the effect of sleep and seizures on interictal spikes, and to use sleep and seizure-related changes in spikes to localize the seizure-onset zone (SOZ). We performed a retrospective analysis of intracranial electroencephalography (EEG) data from patients with focal epilepsy. We automatically detected interictal spikes and we classified different time periods as awake or asleep based on the ratio of alpha to delta power, with a secondary analysis using the recently published SleepSEEG algorithm. We analyzed spike rates surrounding sleep and seizures. We developed a model to localize the SOZ using state-dependent spike rates. We analyzed data from 101 patients (54 women, age range 16-69). The normalized alpha-delta power ratio accurately classified wake from sleep periods (area under the curve=.90). Spikes were more frequent in sleep than wakefulness and in the post-ictal compared to the pre-ictal state. Patients with temporal lobe epilepsy had a greater wake-to-sleep and pre- to post-ictal spike rate increase compared to patients with extra-temporal epilepsy. A machine-learning classifier incorporating state-dependent spike rates accurately identified the SOZ (area under the curve=.83). Spike rates tended to be higher and better localize the seizure-onset zone in non-rapid eye movement (NREM) sleep than in wake or REM sleep. The change in spike rates surrounding sleep and seizures differs between temporal and extra-temporal lobe epilepsy. Spikes are more frequent and better localize the SOZ in sleep, particularly in NREM sleep. Quantitative analysis of spikes may provide useful ancillary data to localize the SOZ and improve surgical planning.

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.

Jointly modeling behavioral and EEG measures of proactive control in task switching.

In this study, we implement joint modeling of behavioral and single-trial electroencephalography (EEG) data derived from a cued-trials task-switching paradigm to test the hypothesis that trial-by-trial adjustment of response criterion can be linked to changes in the event-related potentials (ERPs) elicited during the cue-target interval (CTI). Specifically, we assess whether ERP components associated with preparation to switch task and preparation ofthe relevant task are linked to a response criterion parameter derived from a simple diffusion decision model (DDM). Joint modeling frameworks characterize the brain-behavior link by simultaneously modeling behavioral and neural data and implementing a linking function to bind these two submodels. We examined three joint models: The first characterized the core link between EEG and criterion, the second added a switch preparation input parameter and the third also added a task preparation input parameter. The criterion-EEG link was strongest just before target onset. Inclusion of switch and task preparation parameters did not improve the performance of the criterion-EEG link but was necessary to accurately model the ERP waveform morphology. While we successfully jointly modeled latent model parameters and EEG data from a task-switching paradigm, these findings show that customized cognitive models are needed that are tailored to the multiple cognitive control processes underlying task-switching performance. This is the first paper to implement joint modeling of behavioral measures and single-trial electroencephalography (EEG) data derived from the cue-target interval in a cued-trials task-switching paradigm. Model hyperparameters showed a strong link between response criterion and the pre-target negativity amplitude. Additional parameters (switch preparation, task preparation) were necessary to model the cue-locked ERP waveform morphology. This is consistent with multiple cognitive control processes underlying proactive control and points to the need for more nuanced models of task-switching performance.

Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning

Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that improve or control associated distress and anxiety. We propose a new Artificial Intelligence (AI) algorithm as a digital prognostic health system that models electroencephalographic (EEG) data in order to predict patients' responses to tinnitus therapies. The EEG data was collected from patients prior to treatment and 3-months following a sound-based therapy. Feature selection techniques were utilised to identify predictive EEG variables with the best accuracy. The patients' EEG features from both the frequency and functional connectivity domains were entered as inputs that carry knowledge extracted from EEG into AI algorithms for training and predicting therapy outcomes. The AI models differentiated the patients' outcomes into either therapy responder or non-responder, as defined by their Tinnitus Functional Index (TFI) scores, with accuracies ranging from 98%-100%. Our findings demonstrate the potential use of AI, including deep learning, for predicting therapy outcomes in tinnitus. The research suggests an optimal configuration of the EEG sensors that are involved in measuring brain functional changes in response to tinnitus treatments. It identified which EEG electrodes are the most informative sensors and how the EEG frequency and functional connectivity can better classify patients into the responder and non-responder groups. This has potential for real-time monitoring of patient therapy outcomes at home.

Error-related brain activity in pediatric major depressive disorder: An ERP and time-frequency investigation

BackgroundThe error-related negativity (ERN) reflects individual differences in error monitoring. However, findings on the ERN in adult and adolescent depression have been inconsistent. Analyzing electroencephalographic (EEG) data in both the time- and time-frequency domain can be useful to better quantify neural response to errors. The present study aimed at examining electrocortical measures of error monitoring in early adolescents with and without depression. MethodEEG activity was collected during an arrowhead version of the flanker task in 29 (25 females) early adolescents with depression and 34 without MDD (29 females). ResultsThe depression group showed reduced ERN amplitude, reduced error-related theta power and increased error-related beta power compared to the control group. When all variables that related to MDD diagnosis were considered simultaneously, both theta and beta power, but not the ERN, were independently related to an increased likelihood of being diagnosed with depression. ConclusionsBy examining both time-domain and separate time-frequency measures, the present study provided novel evidence on error monitoring alterations in youth depression, suggesting that depression during adolescence may be characterized by reduced error monitoring (i.e., reduced ERN and error-related theta) and post-error inhibition (i.e., greater error-related beta power). These results support that time-frequency measures might be better suited for examining error-related neural activity in MDD relative to time-domain measures.

SSAVE: A tool for analysis and visualization of sleep periods using electroencephalography data

SSAVE: A tool for analysis and visualization of sleep periods using electroencephalography data

Eliminating or Shortening the Calibration for a P300 Brain-Computer Interface Based on a Convolutional Neural Network and Big Electroencephalography Data: An Online Study.

A brain-computer interface (BCI) measures and analyzes brain activity and converts it into computer commands to control external devices. Traditional BCIs usually require full calibration, which is time-consuming and makes BCI systems inconvenient to use. In this study, we propose an online P300 BCI spelling system with zero or shortened calibration based on a convolutional neural network (CNN) and big electroencephalography (EEG) data. Specifically, three methods are proposed to train CNNs for the online detection of P300 potentials: (i) training a subject-independent CNN with data collected from 150 subjects; (ii) adapting the CNN online via a semisupervised learning/self-training method based on unlabeled data collected during the user's online operation; and (iii) fine-tuning the CNN with a transfer learning method based on a small quantity of labeled data collected before the user's online operation. Note that the calibration process is eliminated in the first two methods and dramatically shortened in the third method. Based on these methods, an online P300 spelling system is developed. Twenty subjects participated in our online experiments. Average accuracies of 89.38%, 94.00% and 93.50% were obtained by the subject-independent CNN, the self-training-based CNN and the transfer learning-based CNN, respectively. These results demonstrate the effectiveness of our methods, and thus, the convenience of the online P300-based BCI system is substantially improved.

Phase Lag Analysis Scalp Electroencephalography May Predict Seizure Frequencies in Patients with Childhood Epilepsy with Centrotemporal Spikes.

Childhood epilepsy with centrotemporal spikes (CECTS) is the most common epilepsy syndrome in school-aged children. However, predictors for seizure frequency are yet to be clarified using the phase lag index (PLI) analyses. We investigated PLI of scalp electroencephalography data at onset to identify potential predictive markers for seizure times. We compared the PLIs of 13 patients with CECTS and 13 age- and sex-matched healthy controls. For the PLI analysis, we used resting-state electroencephalography data (excluding paroxysmal discharges), and analyzed the mean PLIs among all electrodes and between interest electrodes (C3, C4, P3, P4, T3, and T4) and other electrodes. Furthermore, we compared PLIs between CECTS and control data and analyzed the associations between PLIs and total seizure times in CECTS patients. No differences were detected in clinical profiles or visual electroencephalography examinations between patients with CECTS and control participants. In patients with CECTS, the mean PLIs among all electrodes and toward interest electrodes were higher at the theta and alpha bands and lower at the delta and gamma bands than those in control participants. Additionally, the mean PLIs toward interest electrodes in the beta frequency band were negatively associated with seizure times (P = 0.02). The resting-state delta, theta, alpha, and gamma band PLIs might reflect an aberrant brain network in patients with CECTS. The resting-state PLI among the selected electrodes of interest in the beta frequency band may be a predictive marker of seizure times in patients with CECTS.

Histological and electroencephalographic demonstration of probiotic effect for reduce of oxidative stress and apoptosis in experimental traumatic brain injury.

The gut microbiota modulates nervous system function. In the literature, it has been shown that this modula-tion is used in many nervous system injuries through oxidative stress (OS) and apoptosis mechanisms. In this study, it was aimed to investigate the neuroprotective effects of probiotic (PB) treatment in a rat traumatic brain injury (TBI) model with histological and electroencephalographic (EEG) data. Forty male Wistar albino rats were divided into four groups. Group 1 was the control group (CONTROL, n=10) and no trauma was applied. Group 2 was the trauma group with the weight-drop technique (TBH, n=10). Group 3 was the sham group (SHAM), (TBH+sterile saline [SS], n=10) rats were given 500 µL of SS per day by oral gavage. Group 4 was the PB treatment group, (TBH+PB, n=10) rats were treated daily for 7 days with 500 µL of PB oral gavage. Brain samples were collected 7 days after trauma. Histopathological evaluation of brain samples was done with HE. OS with Endothelial nitric oxide synthase, vascularization with Vas-cular Endothelial Growth Factor, gliosis with S100, and apoptosis with caspase 3 were evaluated immunohistochemically. Apoptotic index was determined with TUNEL. In addition, EEG and somatosensory evoked potential (SEP) recording findings were compared. It was determined by HE staining that there was a significant (P<0.001) damage in the TBI and sham groups compared to the control group. It was found that PB treatment provided a significant (P<0.01) improvement in the damage created. While OS (P<0.01), gliosis (P<0.01), and apoptosis (P<0.05) decreased with PB treatment, angiogenesis (P<0.01) increased. In support of these findings, in the software-mediated EEG and SUP examination; Delta wave power and theta/alpha ratio increased with TBI and de-creased with PB treatment. The results showed that PB treatment provided a significant improvement in rats by reducing OS, apoptosis, and gliosis and increasing vascularity. To the best of our knowledge in the literature, it was shown for the 1st time that histological results for the treatment of PB were supported by software-mediated EEG and SEP analysis.

Exoskeleton Training Modulates Complexity in Movement Patterns and Cortical Activity in Able-Bodied Volunteers.

Robot-aided gait training (RAGT) plays a crucial role in providing high-dose and high-intensity task-oriented physical therapy. The human-robot interaction during RAGT remains technically challenging. To achieve this aim, it is necessary to quantify how RAGT impacts brain activity and motor learning. This work quantifies the neuromuscular effect induced by a single RAGT session in healthy middle-aged individuals. Electromyographic (EMG) and motion (IMU) data were recorded and processed during walking trials before and after RAGT. Electroencephalographic (EEG) data were recorded during rest before and after the entire walking session. Linear and nonlinear analyses detected changes in the walking pattern, paralleled by a modulation of cortical activity in the motor, attentive, and visual cortices immediately after RAGT. Increases in alpha and beta EEG spectral power and pattern regularity of the EEG match the increased regularity of body oscillations in the frontal plane, and the loss of alternating muscle activation during the gait cycle, when walking after a RAGT session. These preliminary results improve the understanding of human-machine interaction mechanisms and motor learning and may contribute to more efficient exoskeleton development for assisted walking.

Attentional Engagement During Mobile Application Skill Learning Among Patients With Memory Impairment: A Case Series Exploration.

Attentional engagement is essential for successful cognitive rehabilitation, but little is known about longitudinal interactions with skill learning. To examine how attentional engagement is associated with mobile application skill learning for memory compensation. We hypothesized that patients with greater functional capacity would demonstrate faster learning and attentional engagement drop with skill acquisition, whereas patients with lesser functional capacity would have to maintain attentional engagement to progress throughout training. A case series approach was used with longitudinal skill learning and electroencephalographic (EEG) data recorded across multiple trials and sessions of mobile calendar application training. The study was run in a hospital-based neuropsychology clinic. Seven participants (5 with acquired brain injury, 1 with mild cognitive impairment, and 1 healthy older adult) were recruited. Mobile application operation was trained for the purpose of memory compensation. Skill learning was facilitated through a structured rehabilitation protocol, including large amounts of guided practice with the integration of errorless learning. We quantified learning using the proportion of application steps completed independently at each session. We measured attentional engagement using an EEG marker: the Brain Engagement Index. For fast learners, attentional engagement generally decreased as mobile application learning progressed. In contrast, slow learners exhibited stable engagement over time with consistent, yet much slower, progress in skill learning. The present data indicate that when cognitive impairment is more substantial, skill learning may involve greater attentional engagement. What This Article Adds: Patients undergoing memory rehabilitation may benefit from methods to enhance attentional engagement during skill learning when executive dysfunction is a considerable element of their cognitive profile. Monitoring attentional engagement during cognitive rehabilitation may be useful in identifying and addressing barriers to learning in real time.