HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 5 - Volume 5
Empirical Analysis of EEG and ERPs for Pyschophysiological Adaptive Task Allocation
Empirical Analysis of EEG and ERPs for Pyschophysiological Adaptive Task Allocation
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Analysis of emotion recognition using facial expressions, speech and multimodal information
Proceedings of the 6th international conference on Multimodal interfaces
Feasibility and pragmatics of classifying working memory load with an electroencephalograph
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Decoding Attentional Orientation from EEG Spectra
Proceedings of the 13th International Conference on Human-Computer Interaction. Part I: New Trends
Detecting Frontal EEG Activities with Forehead Electrodes
FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
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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.