Title
Multi-modal affect induction for affective brain-computer interfaces
Author
Mühl, C.
van den Broek, E.L.
Brouwer, A.M.
Nijboer, F.
van Wouwe, N.C.
Heylen, D.
Publication year
2011
Abstract
Reliable applications of affective brain-computer interfaces (aBCI) in realistic, multi-modal environments require a detailed understanding of the processes involved in emotions. To explore the modalityspecific nature of affective responses, we studied neurophysiological responses (i.e., EEG) of 24 participants during visual, auditory, and audiovisual affect stimulation. The affect induction protocols were validated by participants’ subjective ratings and physiological responses (i.e., ECG). Coherent with literature, we found modality-specific responses in the EEG: posterior alpha power decreases during visual stimulation and increases during auditory stimulation, anterior alpha power tends to decrease during auditory stimulation and to increase during visual stimulation. We discuss the implications of these results for multi-modal aBCI.
Subject
Human
PCS - Perceptual and Cognitive Systems TPI - Training & Performance Innovations
BSS - Behavioural and Societal Sciences
Information SocietyInformation Society
affective brain-computer interfaces
emotion
ECG
EEG
visual
auditory
multi-modal
affective brain-computer interfaces
auditory
ECG
EEG
emotion
multi-modal
visual
affective brain-computer interfaces
auditory
emotion
Multi-modal
visual
Electrocardiography
Electrochromic devices
Human computer interaction
Intelligent computing
Interfaces (computer)
Physiological models
Brain computer interface
To reference this document use:
http://resolver.tudelft.nl/uuid:c895ea08-e09a-4ec3-85ab-9ab7a7d50699
DOI
https://doi.org/10.1007/978-3-642-24600-5_27
TNO identifier
463713
Publisher
Springer, Berlin ; Heidelberg
ISBN
9783642246005
Source
Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction, ACII 2011, 9-12 October 2011, Memphis, TN, USA, 6974, 235-245
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Document type
conference paper