Abstract
Emotion is an important aspect in the interaction between humans. It is fundamental to human experience and rational decision-making. There is a great interest for detecting emotions automatically. A number of techniques have been employed for this purpose using channels such as voice and facial expressions. However, these channels are not very accurate because they can be affected by users' intentions. Other techniques use physiological signals along with electroencephalography (EEG) for emotion detection. However, these approaches are not very practical for real time applications because they either ask the participants to reduce any motion and facial muscle movement or reject EEG data contaminated with artifacts. In this paper, we propose an approach that analyzes highly contaminated EEG data produced from a new emotion elicitation technique. We also use a feature selection mechanism to extract features that are relevant to the emotion detection task based on neuroscience findings. We reached an average accuracy of 51% for joy emotion, 53% for anger, 58% for fear and 61% for sadness.
| Original language | English (US) |
|---|---|
| Title of host publication | ACM International Conference Proceeding Series |
| DOIs | |
| State | Published - 2010 |
| Event | 1st Augmented Human International Conference, AH'10 - Megeve, France Duration: Apr 2 2010 → Apr 3 2010 |
Other
| Other | 1st Augmented Human International Conference, AH'10 |
|---|---|
| Country | France |
| City | Megeve |
| Period | 4/2/10 → 4/3/10 |
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Keywords
- affective computing
- brain signals
- feature extraction
- support vector machines
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Software
Cite this
Emotion detection using noisy EEG data. / Mikhail, Mina; El-Ayat, Khaled; El Kaliouby, Rana; Coan, James; Allen, John JB.
ACM International Conference Proceeding Series. 2010. 1785462.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Emotion detection using noisy EEG data
AU - Mikhail, Mina
AU - El-Ayat, Khaled
AU - El Kaliouby, Rana
AU - Coan, James
AU - Allen, John JB
PY - 2010
Y1 - 2010
N2 - Emotion is an important aspect in the interaction between humans. It is fundamental to human experience and rational decision-making. There is a great interest for detecting emotions automatically. A number of techniques have been employed for this purpose using channels such as voice and facial expressions. However, these channels are not very accurate because they can be affected by users' intentions. Other techniques use physiological signals along with electroencephalography (EEG) for emotion detection. However, these approaches are not very practical for real time applications because they either ask the participants to reduce any motion and facial muscle movement or reject EEG data contaminated with artifacts. In this paper, we propose an approach that analyzes highly contaminated EEG data produced from a new emotion elicitation technique. We also use a feature selection mechanism to extract features that are relevant to the emotion detection task based on neuroscience findings. We reached an average accuracy of 51% for joy emotion, 53% for anger, 58% for fear and 61% for sadness.
AB - Emotion is an important aspect in the interaction between humans. It is fundamental to human experience and rational decision-making. There is a great interest for detecting emotions automatically. A number of techniques have been employed for this purpose using channels such as voice and facial expressions. However, these channels are not very accurate because they can be affected by users' intentions. Other techniques use physiological signals along with electroencephalography (EEG) for emotion detection. However, these approaches are not very practical for real time applications because they either ask the participants to reduce any motion and facial muscle movement or reject EEG data contaminated with artifacts. In this paper, we propose an approach that analyzes highly contaminated EEG data produced from a new emotion elicitation technique. We also use a feature selection mechanism to extract features that are relevant to the emotion detection task based on neuroscience findings. We reached an average accuracy of 51% for joy emotion, 53% for anger, 58% for fear and 61% for sadness.
KW - affective computing
KW - brain signals
KW - feature extraction
KW - support vector machines
UR - http://www.scopus.com/inward/record.url?scp=77954461646&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77954461646&partnerID=8YFLogxK
U2 - 10.1145/1785455.1785462
DO - 10.1145/1785455.1785462
M3 - Conference contribution
SN - 9781605588254
BT - ACM International Conference Proceeding Series
ER -