A task-independent workload classifier for neuroadaptive technology: Preliminary data

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Conference Proceeding
2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, 2017, pp. 3171 - 3174
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© 2016 IEEE. Passive brain-computer interfacing allows computer systems direct access to aspects of their user's cognition. In essence, a computer system can gain information about its user without this user needing to explicitly communicate it. Based on this information, human-computer interaction can be made more symmetrical, solving an age-old but still fundamental problem of present-day interaction techniques. For practical real-world application of this technology, it is important that cognitive states can be identified accurately and efficiently. Here we present preliminary data demonstrating it is possible to calibrate a task-independent classifier to identify when a user is under heavy workload across different activities. We used different types of mental arithmetic and even a semantic task. Task-independent classification is an important step towards real-world practical application of this technology.
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