a real-time eeg-based bci system for attention recognition in ubiquitous environment
Several types of biological signal, such as Electroencephalogram (EEG), electrooculogram(EOG), electrocardiogram(ECG), electromyogram (EMG), skin temperature variation and electrodermal activity, may be used to measure a human subject's attention level. Generally electroencephalogram (EEG) is considered the most effective and objective indicator of attention level. However, few systems based on EEG have actually been developed to measure attention levels. In this paper we describe a pervasive system, based on an electroencephalogram (EEG) Brain-Computer Interface, which measures attention level. After demonstrating the effectiveness of our system we then go on to compare our approach with traditional approaches. In our study, three attention levels were classified by a KNN classifier based on the Self-Assessment Manikin (SAM) model. In our experiment, subjects were given several mental tasks to undertake and asked to report on their attention level during the tasks using a set of attention classifications. The average accuracy rate is shown to reach 57.03\% after seven sessions' EEG training. Moreover, our system works in real-time while maintaining this accuracy. This is demonstrated by our time performance evaluation results which show that the time latency is short enough for our system to recognize attention in real-time.
eeg bci attention distance learning