EEG-based workload estimation across affective contexts

@inproceedings{Mhl2014EEGbasedWE,
  title={EEG-based workload estimation across affective contexts},
  author={Christian M{\"u}hl and Camille Jeunet and Fabien Lotte},
  booktitle={Front. Neurosci.},
  year={2014}
}
Workload estimation from electroencephalographic signals (EEG) offers a highly sensitive tool to adapt the human-computer interaction to the user state. To create systems that reliably work in the complexity of the real world, a robustness against contextual changes (e.g., mood), has to be achieved. To study the resilience of state-of-the-art EEG-based workload classification against stress we devise a novel experimental protocol, in which we manipulated the affective context (stressful/non… CONTINUE READING
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