Emotional autobiographical memory retrieval in time domain.

Autobiographical memory (AM) is an important psychological phenomenon that has significance for self-development and mental health. The psychological mechanisms of emotional AM retrieval and their association with individual emotional symptoms remain largely unclear in the literature. For this purpose, the current study provided cue words to elicit emotional AMs. Event-related potentials (ERPs) associated with the retrieval process of AMs were recorded and analyzed. We found that the ERP component N400 was sensitive to both emotional valence and retrieval state, such that its amplitude was larger for negative compared to positive AMs, and larger responses for unrecalled compared to recalled AMs. Further, the N400 amplitude in the positive recalled condition was correlated with individual difference in depression (measured by the Beck Depression Inventory). Another ERP component, the late positive potential (LPP), was also sensitive to emotional valence, such that its amplitude was larger (i.e., more positive-going) for positive compared to negative cues. No significant effect was observed on the early ERP components P1, N1, or P2. The current findings bring new understanding on the difference between positive and negative AMs retrieval in the time domain. Also, the importance of this difference to the individual level of depression is worth noting.

Decoding study-independent mind-wandering from EEG using convolutional neural networks.

Objective. Mind-wandering is a mental phenomenon where the internal thought process disengages from the external environment periodically. In the current study, we trained EEG classifiers using convolutional neural networks (CNNs) to track mind-wandering across studies.Approach. We transformed the input from raw EEG to band-frequency information (power), single-trial ERP (stERP) patterns, and connectivity matrices between channels (based on inter-site phase clustering). We trained CNN models for each input type from each EEG channel as the input model for the meta-learner. To verify the generalizability, we used leave-N-participant-out cross-validations (N= 6) and tested the meta-learner on the data from an independent study for across-study predictions.Main results. The current results show limited generalizability across participants and tasks. Nevertheless, our meta-learner trained with the stERPs performed the best among the state-of-the-art neural networks. The mapping of each input model to the output of the meta-learner indicates the importance of each EEG channel.Significance. Our study makes the first attempt to train study-independent mind-wandering classifiers. The results indicate that this remains challenging. The stacking neural network design we used allows an easy inspection of channel importance and feature maps.

Distinguishing vigilance decrement and low task demands from mind-wandering: A machine learning analysis of EEG.

Mind-wandering is a ubiquitous mental phenomenon that is defined as self-generated thought irrelevant to the ongoing task. Mind-wandering tends to occur when people are in a low-vigilance state or when they are performing a very easy task. In the current study, we investigated whether mind-wandering is completely dependent on vigilance and current task demands, or whether it is an independent phenomenon. To this end, we trained support vector machine (SVM) classifiers on EEG data in conditions of low and high vigilance, as well as under conditions of low and high task demands, and subsequently tested those classifiers on participants' self-reported mind-wandering. Participants' momentary mental state was measured by means of intermittent thought probes in which they reported on their current mental state. The results showed that neither the vigilance classifier nor the task demands classifier could predict mind-wandering above-chance level, while a classifier trained on self-reports of mind-wandering was able to do so. This suggests that mind-wandering is a mental state different from low vigilance or performing tasks with low demands-both which could be discriminated from the EEG above chance. Furthermore, we used dipole fitting to source-localize the neural correlates of the most import features in each of the three classifiers, indeed finding a few distinct neural structures between the three phenomena. Our study demonstrates the value of machine-learning classifiers in unveiling patterns in neural data and uncovering the associated neural structures by combining it with an EEG source analysis technique.

Predicting task-general mind-wandering with EEG.

Mind-wandering refers to the process of thinking task-unrelated thoughts while performing a task. The dynamics of mind-wandering remain elusive because it is difficult to track when someone's mind is wandering based only on behavior. The goal of this study is to develop a machine-learning classifier that can determine someone's mind-wandering state online using electroencephalography (EEG) in a way that generalizes across tasks. In particular, we trained machine-learning models on EEG markers to classify the participants' current state as either mind-wandering or on-task. To be able to examine the task generality of the classifier, two different paradigms were adopted in this study: a sustained attention to response task (SART) and a visual search task. In both tasks, probe questions asking for a self-report of the thoughts at that moment were inserted at random moments, and participants' responses to the probes were used to create labels for the classifier. The 6 trials preceding an off-task response were labeled as mind-wandering, whereas the 6 trials predicting an on-task response were labeled as on-task. The EEG markers used as features for the classifier included single-trial P1, N1, and P3, the power and coherence in the theta (4-8 Hz) and alpha (8.5-12 Hz) bands at PO7, Pz, PO8, and Fz. We used a support vector machine as the training algorithm to learn the connection between EEG markers and the current mind-wandering state. We were able to distinguish between on-task and off-task thinking with an accuracy ranging from 0.50 to 0.85. Moreover, the classifiers were task-general: The average accuracy in across-task prediction was 60%, which was above chance level. Among all the extracted EEG markers, alpha power was most predictive of mind-wandering.