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#Remepy's mobile app shown to rewire brain, modulate immune system and improve mood in adults with subjective cognitive decline

Data Augmentation for Cognitive Behavioral Therapy: Leveraging ERNIE Language Models using Artificial Intelligence
Bosubabu Sambana, Kondreddygari Archana, Suram Indhra Sena Reddy, Shaik Meethaigar Jameer Basha, Shaik Karishma
https://arxiv.org/abs/2506.23503

Data Augmentation for Cognitive Behavioral Therapy: Leveraging ERNIE Language Models using Artificial Intelligence
Cognitive Behavioral Therapy (CBT) is a proven approach for addressing the irrational thought patterns associated with mental health disorders, but its effectiveness relies on accurately identifying cognitive pathways to provide targeted treatment. In today's digital age, individuals often express negative emotions on social media, where they may reveal cognitive distortions, and in severe cases, exhibit suicidal tendencies. However, there is a significant gap in methodologies designed to analy…

A tangible user interface for assessing cognitive mapping ability
Ehud Sharlin, Benjamin Watson, Steve Sutphen, Lili Liu, Robert Lederer, John Frazer
https://arxiv.org/abs/2506.22597

A tangible user interface for assessing cognitive mapping ability
Wayfinding, the ability to recall the environment and navigate through it, is an essential cognitive skill relied upon almost every day in a person's life. A crucial component of wayfinding is the construction of cognitive maps, mental representations of the environments through which a person travels. Age, disease or injury can severely affect cognitive mapping, making assessment of this basic survival skill particularly important to clinicians and therapists. Cognitive mapping has also been t…

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Deep Learning in Mild Cognitive Impairment Diagnosis using Eye Movements and Image Content in Visual Memory Tasks
Tom\'as Silva Santos Rocha, Anastasiia Mikhailova, Moreno I. Coco, Jos\'e Santos-Victor
https://arxiv.org/abs/2506.23016

Deep Learning in Mild Cognitive Impairment Diagnosis using Eye Movements and Image Content in Visual Memory Tasks
The global prevalence of dementia is projected to double by 2050, highlighting the urgent need for scalable diagnostic tools. This study utilizes digital cognitive tasks with eye-tracking data correlated with memory processes to distinguish between Healthy Controls (HC) and Mild Cognitive Impairment (MCI), a precursor to dementia. A deep learning model based on VTNet was trained using eye-tracking data from 44 participants (24 MCI, 20 HCs) who performed a visual memory task. The model utilizes …

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Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIs
Xiaoxiao Yang, Chan Feng, Jiancheng Chen
https://arxiv.org/abs/2506.23458

Neuro-Informed Joint Learning Enhances Cognitive Workload Decoding in Portable BCIs
Portable and wearable consumer-grade electroencephalography (EEG) devices, like Muse headbands, offer unprecedented mobility for daily brain-computer interface (BCI) applications, including cognitive load detection. However, the exacerbated non-stationarity in portable EEG signals constrains data fidelity and decoding accuracy, creating a fundamental trade-off between portability and performance. To mitigate such limitation, we propose MuseCogNet (Muse-based Cognitive Network), a unified joint …

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