Eye blink artifact detection based on multi-dimensional EEG feature fusion and optimization

Eye blink is the most common artifact in electroencephalogram (EEG), which usually affects the performance of EEG-based applications, such as neurological aided diagnostic analysis. For low spatial resolution EEG signals, current methods generally lack of spatial filtering, leading to a degraded performance. In this paper, a novel eye blink artifact detection algorithm based on multiple EEG feature fusion and PSO optimization is proposed in a few-channel data environment. The forehead FP1 and FP2 electrodes EEGs are decomposed based on empirical mode decomposition (EMD) through autocorrelation coefficients for signal filtering. The EEG variance features are extracted by the Common Spatial Pattern (CSP) filtering to enhance the feature discrimination. The particle swarm optimization (PSO) combined with support vector machine (SVM) is applied for feature fusion and optimization. We evaluate the performance on real recorded EEG dataset by the Children's Hospital of Zhejiang University School of Medicine (CHZU). There contain EEGs with eye blink artifacts of 20 subjects. The results show that the proposed method can achieve the highest accuracy, recall rate, precision and F1 value.