Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice.

A WORLDWIDE ENIGMA STUDY ON EPILEPSY-RELATED GRAY AND WHITE MATTER COMPROMISE ACROSS THE ADULT LIFESPAN.

Objectives: Temporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of grey and white matter structures. Evidence supports a progressive condition although the temporal evolution of TLE is poorly defined. This ENIGMA-Epilepsy study utilized multimodal magnetic resonance imaging (MRI) data to investigate structural alterations in TLE patients across the adult lifespan. We charted both grey and white matter changes and explored the covariance of age-related alterations in both compartments. Methods: We studied 769 TLE patients and 885 healthy controls across an age range of 17-73 years, from multiple international sites. To assess potentially non-linear lifespan changes in TLE, we harmonized data and combined median split assessments with cross-sectional sliding window analyses of grey and white matter age-related changes. Covariance analyses examined the coupling of grey and white matter lifespan curves. Results: In TLE, age was associated with a robust grey matter thickness/volume decline across a broad cortico-subcortical territory, extending beyond the mesiotemporal disease epicentre. White matter changes were also widespread across multiple tracts with peak effects in temporo-limbic fibers. While changes spanned the adult time window, changes accelerated in cortical thickness, subcortical volume, and fractional anisotropy (all decreased), and mean diffusivity (increased) after age 55 years. Covariance analyses revealed strong limbic associations between white matter tracts and subcortical structures with cortical regions. Conclusions: This study highlights the profound impact of TLE on lifespan changes in grey and white matter structures, with an acceleration of aging-related processes in later decades of life. Our findings motivate future longitudinal studies across the lifespan and emphasize the importance of prompt diagnosis as well as intervention in patients.

Electroencephalographic microstates as a potential neurophysiological marker differentiating bilateral from unilateral temporal lobe epilepsy.

OBJECTIVE: Electroencephalographic (EEG) microstate abnormalities have been documented in different neurological disorders. We aimed to assess whether EEG microstates are altered also in patients with temporal epilepsy (TLE) and whether they show different activations in patients with unilateral TLE (UTLE) and bilateral TLE (BTLE). METHODS: Nineteen patients with UTLE, 12 with BTLE, and 15 healthy controls were enrolled. Resting state high-density electroencephalography (128 channels) was recorded for 15 min with closed eyes. We obtained a set of stable scalp maps representing the EEG activity, named microstates, from which we acquired the following variables: global explained variance (GEV), mean duration (MD), time coverage (TC), and frequency of occurrence (FO). Two-way repeated measures analysis of variance was used to compare groups, and Spearman correlation was performed to study the maps in association with the clinical and neuropsychological data. RESULTS: Patients with BTLE and UTLE showed differences in most of the parameters (GEV, MD, TC, FO) of the four microstate maps (A-D) compared to controls. Patients with BTLE showed a significant increase in all parameters for the microstates in Map-A and a decrease in Map-D compared to UTLE and controls. We observed a correlation between Map-A, disease duration, and spatial short-term memory, whereas microstate Map-D was correlated with the global intelligence score and short-term memory performance. SIGNIFICANCE: A global alteration of the neural dynamics was observed in patients with TLE compared to controls. A different pattern of EEG microstate abnormalities was identified in BTLE compared to UTLE, which might represent a distinctive biomarker.

Altered spread of waves of activities at large scale is influenced by cortical thickness organization in temporal lobe epilepsy: a magnetic resonance imaging-high-density electroencephalography study.

Temporal lobe epilepsy is a brain network disorder characterized by alterations at both the structural and the functional levels. It remains unclear how structure and function are related and whether this has any clinical relevance. In the present work, we adopted a novel methodological approach investigating how network structural features influence the large-scale dynamics. The functional network was defined by the spatio-temporal spreading of aperiodic bursts of activations (neuronal avalanches), as observed utilizing high-density electroencephalography in patients with temporal lobe epilepsy. The structural network was modelled as the region-based thickness covariance. Loosely speaking, we quantified the similarity of the cortical thickness of any two brain regions, both across groups and at the individual level, the latter utilizing a novel approach to define the subject-wise structural covariance network. In order to compare the structural and functional networks (at the nodal level), we studied the correlation between the probability that a wave of activity would propagate from a source to a target region and the similarity of the source region thickness as compared with other target brain regions. Building on the recent evidence that large-waves of activities pathologically spread through the epileptogenic network in temporal lobe epilepsy, also during resting state, we hypothesize that the structural cortical organization might influence such altered spatio-temporal dynamics. We observed a stable cluster of structure-function correlation in the bilateral limbic areas across subjects, highlighting group-specific features for left, right and bilateral temporal epilepsy. The involvement of contralateral areas was observed in unilateral temporal lobe epilepsy. We showed that in temporal lobe epilepsy, alterations of structural and functional networks pair in the regions where seizures propagate and are linked to disease severity. In this study, we leveraged on a well-defined model of neurological disease and pushed forward personalization approaches potentially useful in clinical practice. Finally, the methods developed here could be exploited to investigate the relationship between structure-function networks at subject level in other neurological conditions.

Neuropsychological Functioning in Bilateral versus Unilateral Temporal Lobe Epilepsy.

Although relatively specific anatomo-electro-clinical features of temporal lobe epilepsy (TLE) with bilateral ictal involvement (bitemporal epilepsy-BTLE) have been described, differentiating between BTLE and unilateral TLE (UTLE) remains challenging. Surgery is often the treatment of choice for drug-resistant UTLE, whereas its use is more controversial in BTLE. It is currently unclear whether neuropsychological assessment can contribute to the differential diagnosis. We retrospectively reviewed the neuropsychological evaluation of 46 consecutive patients with refractory TLE. Eighteen patients were diagnosed with BTLE on the basis of ictal electro-clinical data, in particular a video EEG recording of at least one seizure simultaneously involving the two temporal lobes without the possibility of lateralizing its onset or at least two different seizures independently arising from the two temporal lobes. Twenty-eight patients were classified as UTLE. Presurgery evaluation data were used in this study. Compared with UTLE, BTLE was associated with a lower intelligence quotient (IQ) and more severe impairment in long-term memory, the latter remaining significant even after controlling for IQ. No significant differences were found between right and left UTLE. In conclusion, BTLE and UTLE are associated with relatively distinct neuropsychological profiles, further supporting their classification as different disorders within the TLE spectrum.

Altered spreading of neuronal avalanches in temporal lobe epilepsy relates to cognitive performance: A resting-state hdEEG study.

OBJECTIVE: Large aperiodic bursts of activations named neuronal avalanches have been used to characterize whole-brain activity, as their presence typically relates to optimal dynamics. Epilepsy is characterized by alterations in large-scale brain network dynamics. Here we exploited neuronal avalanches to characterize differences in electroencephalography (EEG) basal activity, free from seizures and/or interictal spikes, between patients with temporal lobe epilepsy (TLE) and matched controls. METHOD: We defined neuronal avalanches as starting when the z-scored source-reconstructed EEG signals crossed a specific threshold in any region and ending when all regions returned to baseline. This technique avoids data manipulation or assumptions of signal stationarity, focusing on the aperiodic, scale-free components of the signals. We computed individual avalanche transition matrices to track the probability of avalanche spreading across any two regions, compared them between patients and controls, and related them to memory performance in patients. RESULTS: We observed a robust topography of significant edges clustering in regions functionally and structurally relevant for the TLE, such as the entorhinal cortex, the inferior parietal and fusiform area, the inferior temporal gyrus, and the anterior cingulate cortex. We detected a significant correlation between the centrality of the entorhinal cortex in the transition matrix and the long-term memory performance (delay recall Rey-Osterrieth Complex Figure Test). SIGNIFICANCE: Our results show that the propagation patterns of large-scale neuronal avalanches are altered in TLE during the resting state, suggesting a potential diagnostic application in epilepsy. Furthermore, the relationship between specific patterns of propagation and memory performance support the neurophysiological relevance of neuronal avalanches.

Little fast, little slow, should I stay or should I go? Adapting cognitive control to local-global temporal prediction across typical development.

Adaptive cognitive control (CC), the ability to adjust goal-directed behavior according to changing environmental demand, can be instantiated bottom-up by implicit knowledge, including temporal predictability of task-relevant events. In S1-S2 tasks, either local (trial-by-trial hazard expectation) or global (block-by-block expectation) temporal information can induce prediction, allowing for proactive action control. Recent developmental evidence showed that adaptive CC based on global temporal prediction emerges earlier than when it is based on the local one only. However, very little is known about how children learn to dynamically adjust behavior on the fly according to changing global predictive information. Addressing this issue is nevertheless crucial to unravel the mechanisms underlying adaptive CC flexibility. Here we used a modified version of the Dynamic Temporal Prediction task to investigate how typically developing younger (6-8 years) and older children (9-11 years), adolescents (12-15 years) and adults (21-31 years) use global prediction to shape adaptive CC over time. Specifically, the short-long percentage of S2 preparatory intervals was manipulated list-wide to create a slow-fast-slow-fast fixed block sequence and test how efficiently the response speed adapted accordingly. Overall, results revealed that in all groups behavioral performance is successfully adjusted as a function of global prediction in the late phase of the task (block 3 to 4). Remarkably, only adolescents and adults exhibit an early adaptation of adaptive CC (block 1 to 2), while children younger than 11 show sluggish ability in inferring implicit changes in global predictive rules. This age-related dissociation suggests that, although being present from an early age, adaptive CC based on global predictive information needs more developmental space to become flexible in an efficient way. In the light of a neuroconstructivist approach, we suggest that bottom-up driven implicit flexibility may represent a key prerequisite for the development of efficient explicit cognitive control.

Resting state network dynamic reconfiguration and neuropsychological functioning in temporal lobe epilepsy: An HD-EEG investigation.

Temporal lobe epilepsy (TLE) is nowadays considered a network disorder impacting several cognitive domains. In this work we investigated dynamic network reconfiguration differences in patients with unilateral TLE compared to a healthy control group, focusing on two connectivity indices: flexibility and integration. We apply these indices for the first time to high-density EEG source-based functional connectivity. We observed that patients with TLE exhibited significantly lower flexibility than healthy controls in the Control, Default Mode and Attentive Dorsal networks, expressed in the delta, theta and alpha bands. In addition, patients with TLE displayed greater integration values across the majority of the resting state networks, especially in the delta, theta and gamma bands. Relevantly, a higher integration index in the Control, Attentive Dorsal and Visual networks in the delta band was correlated with lower performance in visual attention and executive functions. Moreover, a greater integration index in the gamma band of the Control, Somatomotor and Temporoparietal networks was related to lower long-term memory performance. These results suggest that patients with TLE display dysregulated network reconfiguration, with lower flexibility in the brain areas related to cognitive control and attention, together with excessive inter-network communication (integration index). Finally, the correlation between network integration and the reduced cognitive performance suggests a potential mechanism underlying specific alterations in neuropsychological profile of patients with TLE.

Degenerate pathway for processing smile and other emotional expressions in congenital facial palsy: an hdEEG investigation.

Influential theoretical models argue that an internal simulation mechanism (motor or sensorimotor simulation) supports the recognition of facial expressions. However, despite numerous converging sources of evidence, recent studies testing patients with congenital facial palsy (i.e. Moebius syndrome) seem to refute these theoretical models. However, these results do not consider the principles of neuroplasticity and degeneracy that could support the involvement of an alternative neural processing pathway in these patients. In the present study, we tested healthy participants and participants with Moebius syndrome in a highly sensitive facial expression discrimination task and concomitant high-density electroencephalographic recording. The results, both at the scalp and source levels, indicate the activation of two different pathways of facial expression processing in healthy participants and participants with Moebius syndrome, compatible, respectively, with a dorsal pathway that includes premotor areas and a ventral pathway. Therefore, these results support the reactivation of sensorimotor representations of facial expressions (i.e. simulation) in healthy subjects, in the place of an alternative processing pathway in subjects with congenital facial palsy. This article is part of the theme issue 'Cracking the laugh code: laughter through the lens of biology, psychology and neuroscience'.

Adaptive Cognitive Control in Prematurely Born Children: An HD-EEG Investigation.

Preterm birth is a neurodevelopmental risk condition often associated with cognitive control (CC) impairment. Recent evidence showed that CC can be implicitly adapted through associative learning. In the present study we investigated the ability to flexibly adjust CC as a function of implicit stimulus-response temporal regularities in preterm (PT; N = 21; mean age 8 ± 1.3 years; gestational age 30 ± 18.5 weeks) and full-term (FT; N = 20; mean age 8 ± 1.3 years) school-age children. All children underwent an HD-EEG recording while undergoing the Dynamic Temporal Prediction (DTP) task, a simple S1-S2 detection task purposely designed to generate local-global temporal predictability of imperative stimuli. The Wisconsin card sorting test (WCST) was administered to measure explicit CC. The PT group showed more premature and slower (DTP) as well as perseverative (WCST) responses than the FT group. Moreover, pre-terms showed poor adaptive CC as revealed by less efficient global response-speed adjustment. This behavioral pattern was mirrored by a reduced and less sensitive to global manipulation anticipatory Contingent Negative Variation (CNV) and by different cortical source recruitment. These findings suggest that implicit CC may be a reliable endophenotypic marker of atypical cognitive development associated with preterm birth.

Face specific neural anticipatory activity in infants 4 and 9 months old.

The possibility of predicting the specific features of forthcoming environmental events is fundamental for our survival since it allows us to proactively regulate our behaviour, enhancing our chance of survival. This is particularly crucial for stimuli providing socially relevant information for communication and interaction, such as faces. While it has been consistently demonstrated that the human brain shows preferential and ontogenetically early face-evoked activity, it is unknown whether specialized neural routes are engaged by face-predictive activity early in life. In this study, we recorded high-density electrophysiological (ERP) activity in adults and 9- and 4-month-old infants undergoing an audio-visual paradigm purposely designed to predict the appearance of faces or objects starting from congruent auditory cues (i.e., human voice vs nonhuman sounds). Contingent negative variation or CNV was measured to investigate anticipatory activity as a reliable marker of stimulus expectancy even in the absence of explicit motor demand. The results suggest that CNV can also be reliably elicited in the youngest group of 4-month-old infants, providing further evidence that expectation-related anticipatory activity is an intrinsic, early property of the human cortex. Crucially, the findings also indicate that the predictive information provided by the cue (i.e., human voice vs nonhuman sounds) turns into the recruitment of different anticipatory neural dynamics for faces and objects.

Unbalanced functional connectivity at rest affects the ERP correlates of affective prediction in high intolerance of uncertainty individuals: A high density EEG investigation.

In a recent study we outlined the link between Intolerance of Uncertainty (IU) and the neural correlates of affective predictions, as constructed by the brain (generation stage) to prepare to relevant stimuli (implementation stage), and update predictive models according to incoming stimuli (updating stage). In this study we further explored whether the brain's functional organization at rest can modulate neural activity elicited within an emotional S1-S2 paradigm as a function of IU and uncertainty of S1-S2 contingencies. We computed resting state functional connectivity (RS-FC) from a 3-min resting period recorded with high density EEG, and we tested whether RS graph theory nodal measures (i.e., strength, clustering coefficient, betweenness centrality) predicted in-task ERP modulation as a function of IU. We found that RS-FC differently predicted in-task ERPs within the generation and updating stages. Higher IU levels were associated to altered RS-FC patterns within both domain-specific (i.e., right superior temporal sulcus) and domain-general regions (i.e., right orbitofrontal cortex), predictive of a reduced modulation of in-task ERPs in the generation and updating stages. This is presumably ascribable to an unbalancing between synchronization and integration within these regions, which may disrupt the exchange of information between top-down and bottom-up pathways. This altered RS-FC pattern may in turn result in the construction of less efficient affective predictions and a reduced ability to deal with contextual uncertainty in individuals high in IU.

Neuropsychological and behavioral profiles of self-limited epileptic syndromes of childhood: a cross-syndrome comparison.

Childhood epilepsy with centro-temporal spikes (CECTS), Childhood absence epilepsy (CAE) and Panayiotopoulos syndrome (PS) are some of the most common pediatric epileptic syndromes. Despite the relatively benign (self-limited) course of epilepsy, current evidence suggests that these conditions are associated with an increased risk of neuropsychological and behavioral comorbidities. This study provides a cross-epileptic syndromes' comparison reporting on the cognitive and behavioral profile of a cohort of 32 children with CECTS (n = 14), CAE (n = 10) and PS (n = 8), aged 6 to 15 years old. Frequent, although often subclinical cognitive difficulties involving attention, executive functions and academic abilities were found in children with CECTS and CAE, and to a lesser extent in PS. Internalizing symptoms (particularly anxiety) were more common in the PS group compared to CECTS and CAE based on parental reports. Correlational analysis revealed a significant correlation between phonemic fluency and seizure-free interval at the time of evaluation, suggesting a beneficial effect of epilepsy remission on this executive function measure in all the three groups. These results add to existing literature providing further detail on neuropsychological and behavioral peculiarities of children with CECTS, CAE, and PS. Moreover, the need for neuropsychological assessment as part of the standard childhood epilepsy evaluation is stressed. The results are discussed in the context of the current literature, highlighting areas of consensus and controversies related to the clinical management of these epileptic syndromes as well as directions for future research.

Grounding Adaptive Cognitive Control in the Intrinsic, Functional Brain Organization: An HD-EEG Resting State Investigation.

In a recent study, we used the dynamic temporal prediction (DTP) task to demonstrate that the capability to implicitly adapt motor control as a function of task demand is grounded in at least three dissociable neurofunctional mechanisms: expectancy implementation, expectancy violation and response implementation, which are supported by as many distinct cortical networks. In this study, we further investigated if this ability can be predicted by the individual brain's functional organization at rest. To this purpose, we recorded resting-state, high-density electroencephalography (HD-EEG) in healthy volunteers before performing the DTP task. This allowed us to obtain source-reconstructed cortical activity and compute whole-brain resting state functional connectivity at the source level. We then extracted phase locking values from the parceled cortex based on the Destrieux atlas to estimate individual functional connectivity at rest in the three task-related networks. Furthermore, we applied a machine-learning approach (i.e., support vector regression) and were able to predict both behavioral (response speed and accuracy adaptation) and neural (ERP modulation) task-dependent outcome. Finally, by exploiting graph theory nodal measures (i.e., degree, strength, local efficiency and clustering coefficient), we characterized the contribution of each node to the task-related neural and behavioral effects. These results show that the brain's intrinsic functional organization can be potentially used as a predictor of the system capability to adjust motor control in a flexible and implicit way. Additionally, our findings support the theoretical framework in which cognitive control is conceived as an emergent property rooted in bottom-up associative learning processes.

Developmental and epilepsy spectrum of Poirier-Bienvenu neurodevelopmental syndrome: Description of a new case study and review of the available literature.

AIM: To better characterize the clinical phenotype of Poirier-Bienvenu neurodevelopmental syndrome (OMIM ID: 618,732) due to pathogenic variants of the CSNK2B gene. METHOD: We reviewed the electro-clinical and developmental data of all 14 patients with de novo mutations of the CSNK2B gene reported in the literature and describe a further individual with a novel CSNK2B pathogenic variant. RESULTS: Clustered generalized tonic-clonic or myoclonic seizures with onset before the age of 18 months and delayed neurodevelopment were present in more than 75% of patients. Epilepsy was pharmaco-resistant in 40%. All the individuals (27%) with normal neurological development had pharmaco-sensitive epilepsy. The severity of cognitive and motor impairments was higher in the group with pharmaco-resistant epilepsy, and a statistically significant correlation between seizure control and the severity of cognitive impairment was documented (χ2(3) = 9.44; p = .024) INTERPRETATION: Early seizure onset, clustered seizures and delayed development in both males and females were early clinical markers in most patients with CSNK2B mutations. The entity of neurodevelopmental abnormalities was related to epilepsy severity. Prospective studies are required to better assess the relationship between epilepsy and developmental outcomes in this condition.

Investigation of dynamic functional connectivity of the source reconstructed epileptiform discharges in focal epilepsy: A graph theory approach.

OBJECTIVE: The aim of the present study is to investigate with noninvasive methods the modulation of dynamic functional connectivity during interictal epileptiform discharge (IED). METHOD: We reconstructed the cortical source of the EEG recorded IED of 17 patients with focal epilepsy. We then computed dynamic connectivity using the time resolved phase locking value (PLV). We derived graph theory indices (i.e. degree, strength, local efficiency, clustering coefficient and global efficiency). Finally, we selected the atlas node with the maximum activation as the IED cortical source investigating the graph indices dynamics in theta, alpha, beta and gamma frequency bands. RESULTS: We observed IED-locked modulations of the graph indexes depending on the frequency bands. We detected a modulation of the strength, clustering coefficient, local and global efficiency both in theta and in alpha bands, which also displayed modulations of the degree index. In the beta band only the global efficiency was modulated by the IED, while no effects were detected in the gamma band. Finally, we found a correlation between alpha and theta local efficiency, as well as alpha global efficiency, and the epilepsy duration. SIGNIFICANCE: Our findings suggest that the neural synchronization is not limited to the IED cortical source, but implies a phase synchronization across multiple brain areas. We hypothesize that the aberrant electrical activity originating from the IED locus is spread amongst the other network nodes throughout the low frequency bands (i.e. theta and alpha). Moreover, IED-dependent increase in the global efficiency indicates that the IED interfere with the whole network functioning. We finally discussed possible application of this methodology for future investigation.

How time shapes cognitive control: A high-density EEG study of task-switching.

Task-switching is one of the most popular paradigms to investigate cognitive control. The main finding of interest is the switch cost: RTs in switch trials are longer than RTs in repetition trials. Despite the massive amount of research in these topics, little is known about the underlying temporal dynamics of the cortical regions involved in these phenomena. Here we used high density EEG to unveil the spatiotemporal neural dynamics associated with both the switch cost and to its modulation over time (time-on-task effect), as two markers of cognitive control reflecting effortful and procedural mechanisms, respectively. We found that, as a function of task practice, the switch cost decreased and both the switch-positivity and the switch-negativity event-related responses increased, although the latter showed a larger modulatory effect. At a source level, this effect was revealed by a progressively higher activation of the left middle and superior frontal gyrus.

Implicit cognitive flexibility in self-limited focal epilepsy of childhood: An HD-EEG study.

Self-limited focal epilepsy of childhood (SFEC) is often related to mild impairments in several neuropsychological domains, including cognitive flexibility, which is generally considered a process requiring volition and attention. However, recent evidence showed that it can be implicitly adjusted exploiting simple 'stimulus-response' associations as for example, the probability of the stimulus occurrence. Here, we evaluated the capability to implicitly extract environmental patterns of regularities and use them to flexibly adjust proactive control motor control. We tested 21 children with epilepsy (total IQ > 80; 13 with Childhood epilepsy with centro-temporal spikes, 8 with Panayiotopoulos syndrome (PS); 5-13 years old) compared to a healthy age-matched control group (32 participants). We used the Dynamic Temporal Prediction (DTP) task to investigate how behavioral performance is implicitly shaped by the manipulation of the stimulus occurrence probability over time. We recorded EEG to identify neural markers to differentiate the two groups. SFEC group showed a reduction in accuracy (p = .0013) and response speed (p < .001) as well as an absence of response adjustment (p = .65) in relation to the implicit changes in stimulus probability occurrence, in comparison to the control group. The epilepsy group performance in the DTP showed a significant correlation with the phonemic fluency (r = -0.50) and the Perseverations index of the CPT test (r = 0.53). Finally, children with SFEC did not show the modulation of the contingent negative variation (CNV) evoked potential. Overall, children with SFEC showed poor implicit flexibility compared to a control group. This pattern is individually related to high-level executive function, suggesting to extend neuropsychological assessment to the implicit domain.

Electrophysiological correlates of attentional monitoring during a complex driving simulation task.

Starting from the evidence that complex tasks (e.g., driving) require lots of cognitive resources, this research aims at assessing the change of attentional electrophysiological correlates during an oddball task performed while driving a simulator. Twenty-four participants drove along six courses on a moped simulator, preceded by a baseline condition (i.e., watching a video clip of one driving course). Throughout the task, an auditory passive multi-feature oddball with both traffic-related and unrelated stimuli was presented, and the EEG activity was recorded along with driving performance indexes. The main results point out that, as participants learn to drive safely, more attentional resources are available to process the deviant oddball stimuli, as shown by the increase in the amplitude of mismatch negativity (deviant pure tones) and P3a (traffic-related sounds) in the second block of driving. We interpreted these effects as dependent on stimuli complexity and salience.

Applying machine learning EEG signal classification to emotion­related brain anticipatory activity.

Machine learning approaches have been fruitfully applied to several neurophysiological signal classification problems. Considering the relevance of emotion in human cognition and behaviour, an important application of machine learning has been found in the field of emotion identification based on neurophysiological activity. Nonetheless, there is high variability in results in the literature depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight into machine learning applied to emotion identification based on electrophysiological brain activity. For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features. Furthermore, we also contrasted the performance of static and dynamic (time evolving) approaches. The best static feature-classifier combination was the SVM with spectral features (51.8%), followed by LDA with spectral features (51.4%) and kNN with temporal features (51%). The best dynamic feature classifier combination was the SVM with temporal features (63.8%), followed by kNN with temporal features (63.70%) and LDA with temporal features (63.68%). The results show a clear increase in classification accuracy with temporal dynamic features.