Multi-Resolution Wavelet Fractal Analysis and Subtask Training for Enhancing Few-Shot Noisy Brainwave Recognition.

The integration of healthcare monitoring with Internet of Things (IoT) networks radically transforms the management and monitoring of human well-being. Portable and lightweight electroencephalography (EEG) systems with fewer electrodes have improved convenience and flexibility while retaining adequate accuracy. However, challenges emerge when dealing with real-time EEG data from IoT devices due to the presence of noisy samples, which impedes improvements in brainwave detection accuracy. Moreover, high inter-subject variability and substantial variability in EEG signals present difficulties for conventional data augmentation and subtask learning techniques, leading to poor generalizability. To address these issues, we present a novel framework for enhancing EEG-based recognition through multi-resolution data analysis, capturing features at different scales using wavelet fractals. The original data can be expanded many times after continuous wavelet transform (CWT) and recombination, alleviating insufficient training samples. In the transfer stage of deep learning (DL) models, we adopt a subtask learning approach to train the recognition model to generalize efficiently. This incorporates wavelets at various scales instead of exclusively considering average prediction performance across scales and paradigms. Through extensive experiments, we demonstrate that our proposed DL-based method excels at extracting features from small-scale and noisy EEG data. This significantly improves healthcare monitoring performance by mitigating the impact of noise introduced by the external environment.