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Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in understanding neurological disorders. With the advancement of artificial intelligence methods and machine learning techniques, the potential for accurate data-driven diagnoses and effective treatments has grown significantly. However, applying machine learning algorithms to real-world datasets presents diverse challenges at multiple levels. The scarcity of labeled data, especially in low regime scenarios with limited availability of real patient cohorts due to high costs of recruitment, underscores the vital deployment of scaling and transfer learning techniques. In this study, we explore a real-world pathology classification task to highlight the effectiveness of data and model scaling and cross-dataset knowledge transfer. As such, we observe varying performance improvements through data scaling, indicating the need for careful evaluation and labeling. Additionally, we identify the challenges of possible negative transfer and emphasize the significance of some key components to overcome distribution shifts and potential spurious correlations and achieve positive transfer. We see improvement in the performance of the target model on the target (NMT) datasets by using the knowledge from the source dataset (TUAB) when a low amount of labeled data was available. Our findings demonstrated that a small and generic model (e.g. ShallowNet) performs well on a single dataset, however, a larger model (e.g. TCN) performs better in transfer learning when leveraging a larger and more diverse dataset., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Aviation safety depends on the skill and expertise of pilots to meet the task demands of flying an aircraft in an effective and efficient manner. During flight training, students may respond differently to imposed task demands based on individual differences in capacity, physiological arousal, and effort. To ensure that pilots achieve a common desired level of expertise, training programs should account for individual differences to optimize pilot performance. This study investigates the relationship between task performance and physiological correlates of effort in ab initio pilots. Twenty-four participants conducted a flight simulator task with three difficulty levels and were asked to rate their perceived demand and effort using the NASA TLX. We recorded heart rate, EEG brain activity, and pupil size to assess changes in the participants' mental and physiological states across different task demands. We found that, despite group-level correlations between performance error and physiological responses, individual differences in physiological responses to task demands reflected different levels of participant effort and task efficiency. These findings suggest that physiological monitoring of student pilots might provide beneficial insights to flight instructors to optimize pilot training at the individual level., (© 2023. Springer Nature Limited.)

Human memory retrieval is one of the brain's most important, and least understood cognitive mechanisms. Traditionally, research on this aspect of memory has focused on the contributions of particular brain regions to recognition responses, but the interaction between regions may be of even greater importance to a full understanding. In this study, we examined patterns of network connectivity during retrieval in a recognition memory task. We estimated connectivity between brain regions from electroencephalographic signals recorded from twenty healthy subjects. A multivariate autoregressive model (MVAR) was used to determine the Granger causality to estimate the effective connectivity in the time-frequency domain. We used GPDC and dDTF methods because they have almost resolved the previous volume conduction and bivariate problems faced by previous estimation methods. Results show enhanced global connectivity in the theta and gamma bands on target trials relative to lure trials. Connectivity within and between the brain's hemispheres may be related to correct rejection. The left frontal signature appears to have a crucial role in recollection. Theta- and gamma-specific connectivity patterns between temporal, parietal, and frontal cortex may disclose the retrieval mechanism. Old/new comparison resulted in different patterns of network connection. These results and other evidence emphasize the role of frequency-specific causal network interactions in the memory retrieval process. Graphical abstract a Schematic of processing workflow which is consists of pre-processing, sliding-window AMVAR modeling, connectivity estimation, and validation and group network analysis. b Co-registration between Geodesic Sensor Net. and 10-20 system, the arrows mention eight regions of interest (Left, Anterior, Inferior (LAI) and Right, Anterior, Inferior (RAI) and Left, Anterior, Superior (LAS) and Right, Anterior, Superior (RAS) and Left, Posterior, Inferior (LPI) and Right, Posterior, Inferior (RPI) and Left, Posterior, Superior (LPS) and Right, Posterior, Superior (RPS)).