The wider use of wearable devices for electroencephalogram (EEG) data capturing providesa very useful way for the monitoring and self-management of human health. However, the large volumesof data with high dimensions cause computational complexity in EEG data processing and pose a greatchallenge to the use of wearable EEG devices in healthcare. This paper proposes a new approach to extract thestructural information of EEG data and tackle the curse of dimensionality of the EEG data. A set of methodsfor dimensionality reduction (DR)-like linear discriminant analysis (LDA) and their improved methodshave been developed for EEG processing in the literature. However, the existing LDA-related methodssuffer from the singularity problem or expensive computational cost, and none of existing methods takeinto consideration the structure of the projection matrix, which is crucial for the extraction of the structuralinformation of the EEG data. In this paper, a new method called a regularized matrix discriminant analysis(R-MDA) is proposed for EEG feature representation and DR. In the R-MDA, the EEG data are representedas a data matrix, and projection vectors are reshaped to be a set of projection matrices stacking together. Byreformulating the LDA as a least-square formulation and imposing specified constraint on each projectionmatrix, the new R-MDA has been constructed to effectively reduce EEG dimensions and capturing thestructural information of the EEG data. Experimental results demonstrate that this new R-MDA outperformsthe existing LDA-related methods, including achieving improved accuracy with significant DR of the EEGdata. This offers an effective way to enable wearable EEG devices be applicable in human-centered healthmonitoring