Determining User State and Mental Task Demand From Electroencephalographic Data


Information about people’s current activity (their user state) and their mental task demand can be used for multiple purposes in meeting, lecture or office scenarios. Depending on the current user state and the level of task demand mobile communication devices such as cell-phones can configure themselves in a way that they notify their owner of an incoming event (e.g. a phone call) only, if this does not disturb him for instance. Furthermore information about user state and task demand of an audience can be used to provide feedback to a speaker about his talk. In this thesis a system is proposed which determines user state and task demand using electroencephalographic data (EEG data). EEG is recorded using either 16 scalp electrodes from a standard recording device which is usually used for clinical purposes, or a headband with only four electrodes over the pre-frontal and frontal cortex, which is much more comfortable to wear. The recorded data is then passed to a computer where features are extracted which represent the frequency content of the signals, features are preprocessed and finally passed to an artificial neural network or to a Support Vector Machine which predict user state and task demand. For the discrimination of the user states resting, listening, perceiving a presentation, reading an article in a magazine, summarizing the read article and performing arithmetic operations classification accuracies of 94.9% in session and subject dependent experiements, 58.9% in subject independent experiments and 62.7% in subject dependent but session independent experiments could be obtained. For the prediction of low and high task demand during the perception of a presentation accuracies of 92.2% in session and subject dependent experiements, 80.0% in subject independent experiments and 87.1% in subject dependent but session independent experiments were achieved. While all these experiments were obtained in offline scenarios, where data had been collected long before the system was trained and tested, also a prototype system has been developed which demonstrates the feasibility of user state identification and task demand assessment in real time.


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