Electroencephalography-based visual decoding has important applications in brain–computer interfaces and cognitive neuroscience, yet the relative effectiveness of different feature extraction methods for sustained visual paradigms remains unclear due to the absence of standardized, multi-dataset comparative evaluations. This study systematically compares eight feature extraction methods across three public EEG datasets: MindBigData MNIST, MindBigData MNIST-8B for digit recognition, and MSS for natural image classification. The methods include coherence, Granger causality, directed transfer function, partially directed coherence, transfer entropy, discrete wavelet transform, empirical wavelet transform (EWT), and wavelet scattering transform. Two deep learning architectures, EEGNet and EEG Conformer, were trained using two pre-processing pipelines, with and without artifact removal. EWT achieved the highest classification accuracy, reaching 97.83% for digit-vs-blank and 77.10% for within-session natural image classification. Connectivity-based methods consistently underperformed, with the best connectivity method (coherence) reaching up to 91.67%, suggesting that spectral power information is more discriminative than inter-channel relationships. Cross-subject generalization remained challenging, with best accuracies near 68%. The findings establish wavelet-based adaptive spectral decomposition as a strong baseline for EEG visual decoding and highlight the need for domain adaptation techniques to address cross-subject variability.
Cesar-Agustin Corona-Patricio, Carolina Reta, J. A. Cantoral-Ceballos· Applied Informatics· 0 citations
Large Language Models (LLMs) are increasingly integrated into agentic workflows that require extended reasoning, persistent state management, coordinated tool use, and controlled execution. As this operational scope expands, a central question emerges: whether probabilistic generation alone can reliably support coherent behavior across interacting system components. This paper addresses that question through a structural diagnostic review of contemporary agentic systems. Starting from LLM-based tutoring as an analytically demanding entry point and extending toward structurally related agent architectures, the paper draws on a five-phase review of N=145 research records. The analysis is organized through the Agentic Structure Taxonomy (AST), which structures the literature across four dimensions: Cognition, Interaction, Orchestration, and Governance. The review identifies five recurrent empirical problem patterns and uses them as abductive diagnostic cues for formulating seven cross-dimensional transition gaps that capture recurrent discontinuities at the boundaries between reasoning, state, control, and execution. From these gaps, fourteen structural constraints are derived across three control domains: state isolation, control alignment, and execution governance. These constraints are interpreted not as prescriptive design mandates, but as analytically derived conditions associated with reducing error propagation across subsystem transitions. The paper argues that reliability in agentic systems is shaped not only by model performance or prompt design, but also by whether the boundaries linking probabilistic reasoning to persistent state, orchestration, and execution are governed by explicit structural conditions.
Christopher Valdez-Cantú, J. A. Cantoral-Ceballos, Joanna Alvarado-Uribe· Applied Informatics· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
