Abstract
| Original language | Undefined |
|---|---|
| Title of host publication | Proceedings 4th International Conference on Affective Computing and Intelligent Interaction (ACII2011), Part II |
| Editors | Sidney D' Mello, Arthur Graesser, Björn Schuller, Jean-Claude Martin |
| Place of Publication | Berlin |
| Publisher | Springer |
| Pages | 435 |
| Number of pages | 1 |
| ISBN (Print) | 978-3-642-24570-1 |
| DOIs | |
| Publication status | Published - 10 Oct 2011 |
| Event | 4th International Conference on Affective Computing and Intelligent Interaction, ASCII 2011 - Memphis, United States Duration: 9 Oct 2011 → 12 Oct 2011 Conference number: 4 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer Verlag |
| Volume | 6975 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 4th International Conference on Affective Computing and Intelligent Interaction, ASCII 2011 |
|---|---|
| Abbreviated title | ASCII |
| Country | United States |
| City | Memphis |
| Period | 9/10/11 → 12/10/11 |
Keywords
- METIS-279197
- Mood
- Affective Computing
- Affective brain-computer interfacing
- IR-78301
- passive brain-computer interfacing
- HMI-MI: MULTIMODAL INTERACTIONS
- adaptive interfaces
- Emotions
- EWI-20671
- mental state
- user state
- cognitive state estimation
- EEG
Cite this
}
Affective Brain-Computer Interfaces (aBCI 2011). / Mühl, C.; Nijholt, Antinus; Allison, Brandan; Dunne, Stephen; Heylen, Dirk K.J.
Proceedings 4th International Conference on Affective Computing and Intelligent Interaction (ACII2011), Part II. ed. / Sidney D' Mello; Arthur Graesser; Björn Schuller; Jean-Claude Martin. Berlin : Springer, 2011. p. 435 (Lecture Notes in Computer Science; Vol. 6975).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Affective Brain-Computer Interfaces (aBCI 2011)
AU - Mühl, C.
AU - Nijholt, Antinus
AU - Allison, Brandan
AU - Dunne, Stephen
AU - Heylen, Dirk K.J.
N1 - eemcs-eprint-20671
PY - 2011/10/10
Y1 - 2011/10/10
N2 - Recently, many groups (see Zander and Kothe. Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general. J. Neural Eng., 8, 2011) have worked toward expanding brain-computer interface (BCI) systems to include not only active control, but also passive mental state monitoring to enhance human computer interaction (HCI). Many studies have shown that brain imaging technologies can reveal information about the affective and cognitive state of a subject, and that the interaction between humans and machines can be aided by the recognition of those user states. New developments including practical sensors, new machine learning software, and improved interaction with the HCI community are leading us to systems that seamlessly integrate passively recorded information to improve interactions with the outside world. To achieve robust passive BCIs, efforts from applied and basic sciences have to be combined. On the one hand, applied fields such as affective computing aim to develop applications that adapt to changes in the user states and thereby enrich interaction, leading to a more natural and effective usability. On the other hand, basic research in neuroscience advances our understanding of the neural processes associated with emotions. Similar advancements are made for more cognitive mental states such as attention, workload, or fatigue.
AB - Recently, many groups (see Zander and Kothe. Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general. J. Neural Eng., 8, 2011) have worked toward expanding brain-computer interface (BCI) systems to include not only active control, but also passive mental state monitoring to enhance human computer interaction (HCI). Many studies have shown that brain imaging technologies can reveal information about the affective and cognitive state of a subject, and that the interaction between humans and machines can be aided by the recognition of those user states. New developments including practical sensors, new machine learning software, and improved interaction with the HCI community are leading us to systems that seamlessly integrate passively recorded information to improve interactions with the outside world. To achieve robust passive BCIs, efforts from applied and basic sciences have to be combined. On the one hand, applied fields such as affective computing aim to develop applications that adapt to changes in the user states and thereby enrich interaction, leading to a more natural and effective usability. On the other hand, basic research in neuroscience advances our understanding of the neural processes associated with emotions. Similar advancements are made for more cognitive mental states such as attention, workload, or fatigue.
KW - METIS-279197
KW - Mood
KW - Affective Computing
KW - Affective brain-computer interfacing
KW - IR-78301
KW - passive brain-computer interfacing
KW - HMI-MI: MULTIMODAL INTERACTIONS
KW - adaptive interfaces
KW - Emotions
KW - EWI-20671
KW - mental state
KW - user state
KW - cognitive state estimation
KW - EEG
U2 - 10.1007/978-3-642-24571-8_55
DO - 10.1007/978-3-642-24571-8_55
M3 - Conference contribution
SN - 978-3-642-24570-1
T3 - Lecture Notes in Computer Science
SP - 435
BT - Proceedings 4th International Conference on Affective Computing and Intelligent Interaction (ACII2011), Part II
A2 - D' Mello, Sidney
A2 - Graesser, Arthur
A2 - Schuller, Björn
A2 - Martin, Jean-Claude
PB - Springer
CY - Berlin
ER -