Machine learning systems in the brain signal processing field of electroencephalography (EEG) have established a good performance in neural signal analysis, emotion recognition systems, seizure detection and brain-computer interface (BCI). One major difficulty that needs to be addressed in real-world applications is that models derived from one EEG dataset will generally suffer a large drop in performance if tested on a different one, as a result of differences in equipment participating in the recordings, electrode placement, subject and demographic differences, environmental noise and individual subject variability. This paper introduces a new hybrid cross-dataset quantum domain adaptation (QDA) framework that combines covariance-based feature extraction, manifold projection, adversarial maximum mean discrepancy (MMD) domain alignment and parametric quantum kernel learning with parameterized quantum circuits. The experimental results validate that the proposed framework can attain 97.8% classification accuracy, which exceeds the accuracy of conventional CNN (89.2%), CNN-QSVM (92.4%), and Transformer (94.6%) models. It also enhances accuracy across all the metrics like precision, recall, and F1-score.
This is an Internet of Things (IoT) based smart helmet system that is designed to improve the safety...
This Idea Hub is a unified web-based platform de- signed to simplify academic project management and enhance collaboration...
This is an Internet of Things (IoT) based smart helmet system that is designed to improve the safety...
This Idea Hub is a unified web-based platform de- signed to simplify academic project management and enhance collaboration...
