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Objective: Motivated by Health Care 4.0, this study aims to reducing the dimensionality of traditional EEG features based on manual extracted features, including statistical features in the time and frequency domains. Methods: A total of 22 multi-scale features were extracted from the UNM and Iowa datasets using a 4th order Butterworth filter and wavelet packet transform. Based on single-channel validation, 29 channels with the highest R2 scores were selected from a pool of 59 common channels. The proposed channel selection scheme was validated on the UNM dataset and tested on the Iowa dataset to compare its generalizability against models trained without channel selection. Results: The experimental results demonstrate that the proposed model achieves an optimal classification accuracy of 100%. Additionally, the generalization capability of the channel selection method is validated through out-of-sample testing based on the Iowa dataset Conclusions: Using single-channel validation, we proposed a channel selection scheme based on traditional statistical features, resulting in a selection of 29 channels. This scheme significantly reduced the dimensionality of EEG feature vectors related to Parkinson’s disease by 50%. Remarkably, this approach demonstrated considerable classification performance on both the UNM and Iowa datasets. For the closed-eye state, the highest classification accuracy achieved was 100%, while for the open-eye state, the highest accuracy reached 93.75%.

To address the problem of poor efficacy in hybrid learning in higher education, this study conducted a mix-method explanatory case study with structural equation modelling to examine a new construct, "perceived pedagogical value" (PPV), and its relationship with the core constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This study recruited 246 undergraduate students from a year 2 computer science course at an English Medium Instruction University in China to investigate their perceptions of using JazzQuiz for interactive hybrid learning using an online survey and semi-structured interviews. One of the key findings suggests that students who have a deeper understanding of technology's pedagogical value (i.e. higher PPV) are more likely to accept and use the technology with reduced efforts through five dimensions. Our contribution to the literature is threefold: (1) the new construct, PPV, extends our understanding of the UTAUT model for interactive hybrid learning; (2) the extended model highlights the role of student PPV in improving learning efficacy; (3) the five dimensions of how PPV influences technology acceptance, and student academic performance clarify the underlying mechanisms of effective interactive hybrid learning. Further studies are needed to test the new model in different contexts.