PMID- 27740494
OWN - NLM
STAT- MEDLINE
DCOM- 20190104
LR  - 20190104
IS  - 1557-9964 (Electronic)
IS  - 1545-5963 (Linking)
VI  - 15
IP  - 1
DP  - 2018 Jan-Feb
TI  - Attention Recognition in EEG-Based Affective Learning Research Using CFS+KNN 
      Algorithm.
PG  - 38-45
LID - 10.1109/TCBB.2016.2616395 [doi]
AB  - The research detailed in this paper focuses on the processing of 
      Electroencephalography (EEG) data to identify attention during the learning 
      process. The identification of affect using our procedures is integrated into a 
      simulated distance learning system that provides feedback to the user with 
      respect to attention and concentration. The authors propose a classification 
      procedure that combines correlation-based feature selection (CFS) and a 
      k-nearest-neighbor (KNN) data mining algorithm. To evaluate the CFS+KNN 
      algorithm, it was test against CFS+C4.5 algorithm and other classification 
      algorithms. The classification performance was measured 10 times with different 
      3-fold cross validation data. The data was derived from 10 subjects while they 
      were attempting to learn material in a simulated distance learning environment. A 
      self-assessment model of self-report was used with a single valence to evaluate 
      attention on 3 levels (high, neutral, low). It was found that CFS+KNN had a much 
      better performance, giving the highest correct classification rate (CCR) of % for 
      the valence dimension divided into three classes.
FAU - Hu, Bin
AU  - Hu B
FAU - Li, Xiaowei
AU  - Li X
FAU - Sun, Shuting
AU  - Sun S
FAU - Ratcliffe, Martyn
AU  - Ratcliffe M
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20161011
PL  - United States
TA  - IEEE/ACM Trans Comput Biol Bioinform
JT  - IEEE/ACM transactions on computational biology and bioinformatics
JID - 101196755
SB  - IM
MH  - *Algorithms
MH  - Attention/*physiology
MH  - Electroencephalography/*methods
MH  - Female
MH  - Humans
MH  - Male
MH  - Models, Statistical
MH  - Pattern Recognition, Automated
MH  - *Signal Processing, Computer-Assisted
MH  - Young Adult
EDAT- 2016/10/16 06:00
MHDA- 2019/01/05 06:00
CRDT- 2016/10/15 06:00
PHST- 2016/10/16 06:00 [pubmed]
PHST- 2019/01/05 06:00 [medline]
PHST- 2016/10/15 06:00 [entrez]
AID - 10.1109/TCBB.2016.2616395 [doi]
PST - ppublish
SO  - IEEE/ACM Trans Comput Biol Bioinform. 2018 Jan-Feb;15(1):38-45. doi: 
      10.1109/TCBB.2016.2616395. Epub 2016 Oct 11.