Summary: This paper provides a comprehensive overview of how psychological stress can be detected through various biosignals. It discusses the physiological processes triggered by stress, which are measurable through signals like EEG, ECG, EDA, and others(7 more bio-signals). The paper aims to establish reliable biosignal indices that can effectively indicate stress levels, emphasizing the need for consistency and robustness in biosignal data features.
Summary: The article introduces Eye-LRCN, a new method for eye blink detection that also evaluates blink completeness using a Long-Term Recurrent Convolutional Network (LRCN). This approach combines a CNN for feature extraction with a bidirectional RNN for sequence learning, and employs a Siamese architecture to handle class imbalance and limited data. Eye-LRCN demonstrates superior performance in blink detection and completeness assessment, and achieves noticeable results in eye state detection.
Summary: The author thoroughly reviewed organization of the default mode network (DMN) and cognitive roles of the DMN (i.e., self-reference, social cognition, memory, mind wandering). Finally, he suggested a new perspective of the DMN function in human cognitition, in which the DMN intergrate and “broadcast” various representations to create coherent “interal narrative”.
Menon, V. (2023). 20 years of the default mode network: A review and synthesis. Neuron.
Summary: Transformers are recently being compared to the brain. Usually, the internal representations (“embeddings”) are adopted for comparisons. However, the authors focused on “transformations” that integrate contextual information across words, and found that they are more layer-specific than the embeddings. It differs from existing research in that it focuses on transformations related to attention instead of embeddings, which has been one of our recent interests.
summary : This study introduces a cognitive model and task controller to enhance human causal inference abilities through controlled learning strategies, including one-shot and incremental learning. It aims to optimize the efficiency of learning causal relationships by manipulating the presentation sequence of stimulus-outcome pairs, with potential applications in cognitive training.
summary : This thesis investigates methods to enhance the adaptivity of reinforcement learning agents based on the prefrontal cortex meta-control theory of the human brain. The proposed Meta-Dyna algorithm is designed to adapt flexibly to changes in the environment and has demonstrated optimal performance in various settings.
Summary: This paper suggested BrainLM which is a foundation model for brain activity dynamics trained on 6,700 hours of fMRI recordings.
Summary: Utilizing large longitudinal neuroimaging cohort (from adolescence to young adulthood) (IMAGEN), they use multitask connectomes to find neuropsychopathological (NP) factor. They also check generalizability of the NP factor with ABCD (and other) datasets.
Summary: This paper presents a comprehensive review of auditory beat stimulation, with a particular focus on the applications and features of binaural beats. Despite the extensive research conducted on binaural beats, there is still a lack of consensus regarding the consistent effects and the underlying neural mechanisms.
Summary: This study investigates the effectiveness of the Mindfulness-Oriented Recovery Enhancement (MORE) program for police opioid users. Experimental results showed that participants exhibited an increase in theta and alpha brain waves during mindfulness meditation, along with improved coherence of mid-frontal theta. These neurophysiological changes were associated with reductions in opioid use, related to self-transcendence induced by mindfulness.
Summary: Although Transformer can be powerful for modeling visual relations and describing complicated patterns, it could still perform unsatisfactorily for video-based facial expression recognition, since the expression movements in a video can be too small to reflect meaningful spatial-temporal relations. They propose to decompose the modeling of expression movements of a video into the modeling of a series of expression snippets, each of which contains a few frames. Their propsed model, Expression Snippet Transformer (EST) process intra-snippet and inter-snippet information seperately and combine them together. Code is available in github.