Decoding Cognitive and Affective States

Jun 1, 2021
AI generated picture for the prompt: Brain, Big Data, Artificial Intelligence, Human

During my internship and my Bachelor’s thesis at the Neurolab, Fraunhofer Institute for Industrial Engineering IAO, we worked on pipelines and algorithms to decode cognitive and affective states based on neurophysiological data (EEG, fNIRS, heart rate, skin conductance, respiration, body temperature, etc.).

Collaborators: Katharina Lingelbach

Machine Learning Affective Neuroscience
Sabrina Gado
Sabrina Gado
PhD student

My research interests combine psychology with technology.

Publications

Workload-dependent Hemispheric Asymmetries during the Emotion-Cognition Interaction: A Close-to-Naturalistic fNIRS Study
Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification
Classifying Cognitive Load in a Quasi-Realistic Scenario Based on Multimodal Neurophysiological Data
Real-time feedback of subjective affect and working memory load based on neurophysiological activity
What I feel and what I say: Decoding neurophysiological correlates of cognitive and affective states