Real-time hybrid ocular artifact detection and removal for single channel EEG

Abstract

Electroencephalography (EEG) is a promising technique to record brain activities in natural settings. However, EEG signals are usually contaminated by Ocular Artifacts (OA) such as eye blink activities. Removal of OA is critical to obtain clean EEG signals required for the feature extraction and classification. With the increasing interest in wearable technologies, single channel EEG systems are becoming more prevalent. Such ambulatory devices require real-time signal processing for immediate feedback. This paper presents a hybrid algorithm to detect and remove OA from single channel EEG signal using NeuroMonitor hardware platform. The algorithm first detects the eye blinks (OA zone) using Algebraic approach, and then removes artifact from OA zone using Discrete Wavelet Transform (DWT) decomposition method. De-noising technique is applied only to the OA zone to keep the critical neural information intact. The OA removal algorithm is applied to the online data for 0.5 sec epoch length. The performance evaluation is carried out qualitatively and quantitatively using time-frequency analysis, mean square coherence and other statistical parameters, i.e. Correlation Coefficient and Mutual Information. Processing time for DWT was significantly lower (x25) to that of SWT. This proposed hybrid OA removal algorithm demonstrates real-time execution with sufficient accuracy.