PMID- 26737113
OWN - NLM
STAT- MEDLINE
DCOM- 20161006
LR  - 20240327
IS  - 2694-0604 (Electronic)
IS  - 1557-170X (Print)
IS  - 2375-7477 (Linking)
VI  - 2015
DP  - 2015 Aug
TI  - A Step towards EEG-based brain computer interface for autism intervention.
PG  - 3767-70
LID - 10.1109/EMBC.2015.7319213 [doi]
AB  - Autism Spectrum Disorder (ASD) is a prevalent and costly neurodevelopmental 
      disorder. Individuals with ASD often have deficits in social communication skills 
      as well as adaptive behavior skills related to daily activities. We have recently 
      designed a novel virtual reality (VR) based driving simulator for driving skill 
      training for individuals with ASD. In this paper, we explored the feasibility of 
      detecting engagement level, emotional states, and mental workload during VR-based 
      driving using EEG as a first step towards a potential EEG-based Brain Computer 
      Interface (BCI) for assisting autism intervention. We used spectral features of 
      EEG signals from a 14-channel EEG neuroheadset, together with therapist ratings 
      of behavioral engagement, enjoyment, frustration, boredom, and difficulty to 
      train a group of classification models. Seven classification methods were applied 
      and compared including Bayes network, naive Bayes, Support Vector Machine (SVM), 
      multilayer perceptron, K-nearest neighbors (KNN), random forest, and J48. The 
      classification results were promising, with over 80% accuracy in classifying 
      engagement and mental workload, and over 75% accuracy in classifying emotional 
      states. Such results may lead to an adaptive closed-loop VR-based skill training 
      system for use in autism intervention.
FAU - Fan, Jing
AU  - Fan J
FAU - Wade, Joshua W
AU  - Wade JW
FAU - Bian, Dayi
AU  - Bian D
FAU - Key, Alexandra P
AU  - Key AP
FAU - Warren, Zachary E
AU  - Warren ZE
FAU - Mion, Lorraine C
AU  - Mion LC
FAU - Sarkar, Nilanjan
AU  - Sarkar N
LA  - eng
GR  - R01 MH091102/MH/NIMH NIH HHS/United States
GR  - 1R01MH091102-01A1/MH/NIMH NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PT  - Research Support, U.S. Gov't, Non-P.H.S.
PL  - United States
TA  - Annu Int Conf IEEE Eng Med Biol Soc
JT  - Annual International Conference of the IEEE Engineering in Medicine and Biology 
      Society. IEEE Engineering in Medicine and Biology Society. Annual International 
      Conference
JID - 101763872
SB  - IM
MH  - Adolescent
MH  - Autism Spectrum Disorder/physiopathology/psychology/*therapy
MH  - Automobile Driving/education
MH  - Bayes Theorem
MH  - *Brain-Computer Interfaces
MH  - Electroencephalography/methods
MH  - Emotions
MH  - Female
MH  - Humans
MH  - Male
MH  - Neural Networks, Computer
MH  - Signal Processing, Computer-Assisted
MH  - Support Vector Machine
MH  - Teaching
MH  - User-Computer Interface
PMC - PMC5600898
MID - NIHMS903661
EDAT- 2016/01/07 06:00
MHDA- 2016/10/08 06:00
PMCR- 2017/09/17
CRDT- 2016/01/07 06:00
PHST- 2016/01/07 06:00 [entrez]
PHST- 2016/01/07 06:00 [pubmed]
PHST- 2016/10/08 06:00 [medline]
PHST- 2017/09/17 00:00 [pmc-release]
AID - 10.1109/EMBC.2015.7319213 [doi]
PST - ppublish
SO  - Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:3767-70. doi: 
      10.1109/EMBC.2015.7319213.