Applying machine learning EEG signal classification to emotion‑related 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 (linear discriminant analysis, support vector machine and k-nearest neighbour) was compared using both spectral and temporal features. Furthermore, we also contrasted the classifiers' performance with static and dynamic (time evolving) features. The results show a clear increase in classification accuracy with temporal dynamic features. In particular, the support vector machine classifiers with temporal features showed the best accuracy (63.8 %) in classifying high vs low arousal auditory stimuli

Applying machine learning EEG signal classification to emotion related 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

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

The impact of selective attention on information maintenance in visual short term memory: a neurofunctional investigation

Two of the most important constructs of cognitive psychology are attention and memory. These are pillars of our cognition, allowing for the selection, encoding and storing of information in order to reach our goals. Attention and memory are nevertheless very broad concepts, both emerging from the interaction of different cognitive mechanisms. In the present work, emphasis has been placed on selective attention and visual short term memory as two main computational stages of information. Furthermore, selective attention both in the temporal and spatial domains was investigated with a special focus on how differently these domains impact the succesful maintenance of visual information in short term memory. Close attention was paid to the neural activity underlying the processes mentioned above. Therefore, high-density electroencephalogram (HD-EEG) was used to provide an optimal compromise between temporal and spatial resolution. The first two chapters of this thesis provide a brief introduction of the concept of visual short term memory (VSTM) and selective attention. Next, the relationship between these two mental processes is examined by discussing some of the most relevant empirical studies on this topic. In the central chapters, it is presented new experimental evidence from two different studies. In the first study, the focus is on the effect of temporal orienting of attention (TO) on memory, targeting the encoding (Experiment 1a) and maintenance (Experiment 1b) of information as two distinct computational steps of VSTM. In the second study, it is further explored the neural patterns underlying the VSTM network identified in the first study, deepening the functional role and the relations of the relative nodes of this circuit with regard to the maintenance of visual information. The final part of the present work is dedicated to discussing the theoretical implication of the empirical findings as well as to identifying new experimental routes to pursue with the aim of extending upon the presented results.Due dei più importanti costrutti della psicologia cognitiva sono l'attenzione e la memoria. Questi sono i pilastri della nostra cognizione, che permettono la selezione, la codifica e lo stoccaggio delle informazioni per raggiungere i nostri obiettivi. Attenzione e memoria sono tuttavia concetti molto ampi, che emergono entrambi dall'interazione di diversi meccanismi cognitivi. Nel presente lavoro, l'enfasi è stata posta sull'attenzione selettiva e sulla memoria visiva a breve termine come due principali stadi computazionali dell'informazione. Inoltre, l'attenzione selettiva sia nel dominio temporale che in quello spaziale è stata indagata con particolare attenzione a come questi domini influenzano in modo diverso l'efficacia del mantenimento dell'informazione visiva nella memoria a breve termine. Particolare attenzione è stata dedicata all'attività neurale alla base dei processi sopra menzionati. Pertanto, l'elettroencefalogramma ad alta densità (HD-EEG) è stato utilizzato per fornire un compromesso ottimale tra risoluzione temporale e spaziale. I primi due capitoli di questa tesi forniscono una breve introduzione al concetto di memoria visiva a breve termine (VSTM) e di attenzione selettiva. Successivamente, la relazione tra questi due processi mentali viene esaminata discutendo alcuni dei più rilevanti studi empirici sull'argomento. Nei capitoli centrali vengono presentate nuove evidenze sperimentali tratte da due diversi studi. Nel primo studio, l'attenzione è focalizzata sull'effetto dell'orientamento temporale dell'attenzione (TO) sulla memoria, puntando alla codifica (Esperimento 1a) e al mantenimento (Esperimento 1b) dell'informazione come due distinti passi computazionali della VSTM. Nel secondo studio vengono ulteriormente esplorati i pattern neurali sottostanti la rete VSTM individuati nel primo studio, approfondendo il ruolo funzionale e le relazioni dei relativi nodi di questo circuito per quanto riguarda il mantenimento dell'informazione visiva. La parte finale del presente lavoro è dedicata alla discussione delle implicazioni teoriche delle scoperte empiriche e all'individuazione di nuovi percorsi sperimentali da perseguire con l'obiettivo di estendersi ai risultati presentati

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

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

n 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

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

International audienc

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

International audienc

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