The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Emotion recognition using brain activity
Abstract
Our project focused on recognizing emotion from human brain activity, measured by EEG signals. We have proposed a system to analyze EEG signals and classify them into 5 classes on two emotional dimensions, valence and arousal. This system was designed using prior knowledge from other research, and is meant to assess the quality of emotion recognition using EEG signals in practice. In order to perform this assessment, we have gathered a dataset with EEG signals. This was done by measuring EEG signals from people that were emotionally stimulated by pictures. This method enabled us to teach our system the relationship between the characteristics of the brain activity and the emotion. We found that the EEG signals contained enough information to separate five different classes on both the valence and arousal dimension. However, using a 3-fold cross validation method for training and testing, we reached classification rates of 32% for recognizing the valence dimension from EEG signals and 37% for the arousal dimension. Much better classification rates were achieved when using only the extreme values on both dimensions, the rates were 71% and 81%.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
Measuring emotion: The self-assessment manikin and the semantic differential
Bradley, M. M., & Lang, P. J. (1994) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/0005-7916(94)90063-9 · Cited by 9,538
Cognitive and emotional influences in anterior cingulate cortex
Bush, G., Luu, P., & Posner, M. I. (2000) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01483-2 · Cited by 6,259
Automatic facial expression analysis: a survey
Fasel, B., & Luettin, J. (2003) · Pattern Recognition · doi:10.1016/s0031-3203(02)00052-3 · Cited by 1,810
Automatic analysis of facial expressions: the state of the art
Pantie, M., & Rothkrantz, L. J. M. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.895976 · Cited by 1,778 · cited 1×
“For example, research has been done to make computers recognize emotion from speech [7], facial expressions [12] or a fusion of both methods [4].”
BackgroundSelf-citationFunctional neuroanatomy of emotions: A meta-analysis
MURPHY, F. C., NIMMO-SMITH, I., & LAWRENCE, A. D. (2003) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/cabn.3.3.207 · Cited by 1,135
Emotion recognition system using short-term monitoring of physiological signals
Kim, K. H., Bang, S. W., & Kim, S. R. (2004) · Medical & Biological Engineering & Computing · doi:10.1007/bf02344719 · Cited by 944
Analysis of emotion recognition using facial expressions, speech and multimodal information
Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, C. M., Kazemzadeh, A., Lee, S., Neumann, U., & Narayanan, S. (2004) · Proceedings of the 6th international conference on Multimodal interfaces · doi:10.1145/1027933.1027968 · Cited by 840 · cited 1×
“For example, research has been done to make computers recognize emotion from speech [7], facial expressions [12] or a fusion of both methods [4].”
Background
Cited by
Feature Extraction and Selection for Emotion Recognition from EEG
Jenke, R., Peer, A., & Buss, M. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2339834 · Cited by 1,081
Wavelet-based emotion recognition system using EEG signal
Mohammadi, Z., Frounchi, J., & Amiri, M. (2017) · Neural Computing and Applications · doi:10.1007/s00521-015-2149-8 · Cited by 361
Petrantonakis, P. C., & Hadjileontiadis, L. J. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.7 · Cited by 260
Feature Extraction and Selection for Emotion Recognition from Electrodermal Activity
Shukla, J., Barreda-Angeles, M., Oliver, J., Nandi, G. C., & Puig, D. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2901673 · Cited by 253
GSR and Blink Features for Cognitive Load Classification
Nourbakhsh, N., Wang, Y., & Chen, F. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40483-2_11 · Cited by 98
Metadata
- Authors
- Horlings, R., Datcu, D., & Rothkrantz, L. J. M.
- Venue
- Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing - CompSysTech '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- II.1
- OpenAlex
- W2067583143
- Cited by
- 262
- References
- 8
- Type
- conference abstract
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Emotion recognition using brain activity. https://app.cochat.ai/papers/10.1145/1500879.1500888
- The paper
- Horlings, R., Datcu, D., & Rothkrantz, L. J. M. (2008). Emotion recognition using brain activity. Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing - CompSysTech '08. https://doi.org/10.1145/1500879.1500888
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
