Using Wireless EEG Signals to Assess Memory Workload in the $n$-Back Task

Assessment of mental workload using physiological measures, especially electroencephalography (EEG) signals, is an active area. Recently, a number of wireless acquisition systems to measure EEG and other physiological signals have become available. Few studies have applied such wireless systems to assess cognitive workload and evaluate their performance. This paper presents an initial step to explore the feasibility of a popular wireless system (Emotiv EPOC headset) to assess memory workload levels in a well-known n-back task. We developed a signal processing and classification framework, which integrated an automatic artifact removal algorithm, a broad spectrum of feature extraction techniques, a personalized feature scaling method, an information-theory-based feature selection approach, and a proximal-support-vector-machine-based classification model. The experimental results show that the wirelessly collected EEG signals can be used to classify different memory workload levels for nine participants. The classification accuracies between the lowest workload level (0-back) and active workload levels (1-, 2-, 3-back) were close to 100%. The best classification accuracy for 1- versus 2-back was 80%, and 1- versus 3-back was 84%. This study indicates that the wireless acquisition system and the advanced data analytics and pattern recognition techniques are promising to achieve real-time monitoring and identification of mental workload levels for humans engaged in a wide variety of cognitive activities in the modern society.