| چکیده انگلیسی مقاله |
Background: Electroencephalography (EEG) is a cornerstone in cognitive neuroscience, providing critical insights into the neural mechanisms underlying skill acquisition. Despite significant advancements in signal processing techniques, extracting meaningful patterns from EEG data —especially in the context of dynamic neural shifts during learning—remains a persistent challenge. Traditional analytical approaches often fail to account for the nonlinear temporal dynamics inherent in learning processes, which limits their ability to decode subtle neural reorganizations. This study addresses this gap by proposing an innovative computational framework based on Rhodonea curves—sinusoidal patterns resembling flower petals—to analyze EEG signals during the acquisition of a complex motor skill: touch-typing by the Colemak keyboard layout.
Objective and Innovation: The study aims to develop and validate a computationally efficient algorithm for classifying EEG data across distinct stages of skill learning. Central to this approach is the introduction of asymmetry indices derived from Rhodonea curves, which quantify nonlinear features of brain activity. This work represents the first application of Rhodonea-based analysis in EEG signal processing, providing a geometrically intuitive and computationally lightweight alternative to conventional nonlinear methods, such as entropy or fractal dimension analysis.
Methodology: The dataset, available on IEEEDataPort, consisted of EEG recordings from 10 participants (6 females and 4 males), focusing on 9 channels (F3, Fz, F4, C3, Cz, C4, P3, POz, P4) collected during 12 typing sessions. Data from sessions 4, 8, and 11—representing the early, intermediate, and advanced learning phases—were analyzed, with each session repeated five times to capture intra-session variability. For the first time, a Rhodonea curve-based method has been introduced for signal analysis, featuring a structure resembling a flower with an adjustable number of petals. The Rhodonea model was parameterized with one to ten petals, and three new indices based on asymmetry in the Rhodonea curve were computed to characterize spatiotemporal variations in EEG signals. A Support Vector Machine (SVM) utilizing a one-vs-all strategy was employed to classify 15 classes (5 repetitions × 3 sessions). Channel-specific optimizations and petal-count analyses were conducted to identify discriminative brain regions and optimal model configurations.
Key Findings: The analysis revealed robust classification performance, with two-class classification achieving accuracies ranging from 79.3% to 93.3%. Optimal results were observed in channels F3, Fz, C3, C4, and POz using a 4-petal Rhodonea configuration. In the three-session classification, the highest accuracy was recorded for the advanced learning phase (Session 11: 92%), followed by the early phase (Session 4: 90%) and the intermediate phase (Session 8: 72.6%). The lower accuracy in Session 8 suggests a transitional neural state marked by unstable skill consolidation, where neither novice nor expert patterns dominate. Neuroanatomically, the frontal (F3, Fz), central (C3, C4), and parieto-occipital (POz) regions demonstrated heightened discriminative power, consistent with prior studies implicating these areas in cognitive control, motor planning, and visuospatial integration during learning. Session-specific activation patterns indicated early-phase prefrontal engagement for attention allocation and advanced-phase parietal consolidation for skill automatization.
Comparative Analysis: This study diverges from prior work by integrating geometric asymmetry metrics—rather than spectral or entropy-based features—to model learning-induced neural plasticity. The computational efficiency and interpretability of Rhodonea-based features (e.g., petal-count visualization) offer distinct advantages for real-time brain-computer interface (BCI) applications. Notably, the intermediate phase’s lower accuracy (72.6%) highlights the methodological challenge of decoding transitional neural states, a limitation underrepresented in earlier literature.
Limitations and Future Directions: This research had limitations that should be considered in future studies. First, the small sample size (N=10) and fixed signal length (1280 samples) may limit generalizability; future work should incorporate larger datasets and variable-length signal analysis. Second, although the non-linear features presented are computationally simple and low-cost, using other complex features might enhance the model's performance. Third, while SVM demonstrated efficacy, comparative studies with deep learning models (e.g., CNNs, LSTMs) could further validate the method’s robustness. Fourth, physiological validation via multimodal neuroimaging (e.g., fMRI/fNIRS) is needed to spatially localize the observed dynamics. Finally, statistical refinements—such as ANOVA or t-tests for feature selection—could enhance model rigor and mitigate overfitting risks.
Conclusion: This research pioneers the application of Rhodonea curves in EEG analysis, establishing a novel framework for decoding the neural correlation of skill learning. The high classification accuracies and neuroanatomically consistent results underscore the method’s potential for both academic research and applied domains, including adaptive learning systems and neurorehabilitation. Future efforts should prioritize large-scale validation and integration with multimodal neuroimaging to advance our understanding of learning-related brain plasticity and refine real-world applications. |