The recent developments in the field of neuroscience and brain-computer interfaces (BCIs) have shown a significant shift towards personalized and multimodal approaches. Researchers are increasingly focusing on integrating various neural data types, such as EEG, MEG, and fMRI, to create foundational models that can decode and encode visual information, as well as convert between different neural modalities. This multimodal approach not only enhances the accuracy of BCI tasks but also opens new avenues for personalized applications that cater to individual user preferences and needs. Additionally, there is a growing interest in leveraging deep learning and artificial intelligence to automate the analysis of neural data, particularly in the context of diagnosing neurological disorders and monitoring cognitive states in educational settings. Notably, innovative techniques such as quantum-inspired neural networks are being explored to better understand the connectivity between brain regions and enhance the semantic information extracted from brain signals. These advancements collectively push the boundaries of what is possible in neuroscience and BCI, paving the way for more robust, accurate, and personalized systems.
Personalized and Multimodal Approaches in Neuroscience and BCI
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Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversion
A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report Generation
EEG Spectral Analysis in Gray Zone Between Healthy and Insomnia
A Multi-Label EEG Dataset for Mental Attention State Classification in Online Learning
Adapting the Biological SSVEP Response to Artificial Neural Networks
User-wise Perturbations for User Identity Protection in EEG-Based BCIs
Detecting Student Disengagement in Online Classes Using Deep Learning: A Review
Investigating the Use of Productive Failure as a Design Paradigm for Learning Introductory Python Programming
Towards Personalized Brain-Computer Interface Application Based on Endogenous EEG Paradigms
AdaptLIL: A Gaze-Adaptive Visualization for Ontology Mapping
Neuro-3D: Towards 3D Visual Decoding from EEG Signals
Gaze2AOI: Open Source Deep-learning Based System for Automatic Area of Interest Annotation with Eye Tracking Data
Quantum-Brain: Quantum-Inspired Neural Network Approach to Vision-Brain Understanding
Deep Feature Response Discriminative Calibration
Exploring the Impact of Quizzes Interleaved with Write-Code Tasks in Elementary-Level Visual Programming