Christian Kohlschein
Papers
1
Total Citations
134
H-Index
1
About
Christian Kohlschein is a leading researcher at the intersection of affective computing and biomedical signal processing. His primary contributions lie in developing robust, algorithmic methods for automatic emotion recognition, with a particular focus on leveraging electroencephalography (EEG) data. His seminal 2016 work, “EEG-based automatic emotion recognition: Feature extraction, selection and classification methods,” has garnered over 134 citations, establishing a foundational framework for the field. In this highly influential paper, Kohlschein systematically analyzed and benchmarked various techniques for extracting meaningful features from brain signals, selecting the most discriminative ones, and applying effective classification models to detect human affect—such as anger or sadness—from neural activity. Beyond this core contribution, his research spans human-robot interaction and the practical deployment of affect detection systems. By bridging the gap between raw neurophysiological data and reliable emotional state inference, Kohlschein’s work has paved the way for more intuitive, responsive technologies in healthcare, assistive robotics, and user experience design, making him a key figure in the advancement of emotion-aware computing.
Research Focus
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Top Papers
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