TY - GEN
T1 - Classifying brain activities in perception of shapeanalogous english letters based on EEG signal
AU - Bose, Rohit
AU - Goh, Sim Kuan
AU - Wong, Kian F.
AU - Thakor, Nitish
AU - Bezerianos, Anastasios
AU - Li, Junhua
N1 - Publisher Copyright:
©2018 IEEE.
PY - 2018/12/26
Y1 - 2018/12/26
N2 - Brain computer interface (BCI) technique has been demonstrated that human intentions or stimulus perception can be recognized using EEG signal recorded from the human scalp. When an intention is initiated in the brain or an external stimulus is perceived, the underlying relevant processing alters brain activity. This alteration in brain activity can be reflected in EEG signal. The intention or stimulus perception is therefore classified based on the alteration in brain activity. It might be difficult to classify brain activities in the perception of shape-analogous English letters because the similar shape could lead to less difference in brain activity. In order to explore classification feasibility and classification performance of shape-analogous letters using EEG signal, we performed an experiment of shape-analogous letter perception, in where participants perceived four letters (i.e., O', O', O' and O') while EEG signal was recorded. The F-score method was employed to assess the discriminative power for each feature, and a subgroup of features with high discriminative powers was then selected and fed into classifiers. Five classifiers (i.e., k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF) and AdaBoost (ADA)), which are either pervasive or advanced in the field of machine learning, were utilized to classify brain activities in perception of shape-analogous letters. For each classifier, its parameters and the number of used features were optimized. Based on the performance comparison among the classifiers, Random Forest (RF) classifier achieved a maximal accuracy of 74.1%, but it was not statistically significantly better than the SVM. Our study demonstrated that brain activities in perception of shape-analogous English letters can be classified based on EEG signal and showed that random forest classifier outperformed other classifiers according to the results of comparison.
AB - Brain computer interface (BCI) technique has been demonstrated that human intentions or stimulus perception can be recognized using EEG signal recorded from the human scalp. When an intention is initiated in the brain or an external stimulus is perceived, the underlying relevant processing alters brain activity. This alteration in brain activity can be reflected in EEG signal. The intention or stimulus perception is therefore classified based on the alteration in brain activity. It might be difficult to classify brain activities in the perception of shape-analogous English letters because the similar shape could lead to less difference in brain activity. In order to explore classification feasibility and classification performance of shape-analogous letters using EEG signal, we performed an experiment of shape-analogous letter perception, in where participants perceived four letters (i.e., O', O', O' and O') while EEG signal was recorded. The F-score method was employed to assess the discriminative power for each feature, and a subgroup of features with high discriminative powers was then selected and fed into classifiers. Five classifiers (i.e., k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF) and AdaBoost (ADA)), which are either pervasive or advanced in the field of machine learning, were utilized to classify brain activities in perception of shape-analogous letters. For each classifier, its parameters and the number of used features were optimized. Based on the performance comparison among the classifiers, Random Forest (RF) classifier achieved a maximal accuracy of 74.1%, but it was not statistically significantly better than the SVM. Our study demonstrated that brain activities in perception of shape-analogous English letters can be classified based on EEG signal and showed that random forest classifier outperformed other classifiers according to the results of comparison.
KW - AdaBoost
KW - Classification
KW - EEG
KW - Fscore
KW - K-nearest neighbors
KW - Linear discriminant classifier
KW - Random Forest
KW - Shape analogous letters
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85061441694&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061441694&partnerID=8YFLogxK
U2 - 10.1109/CW.2018.00043
DO - 10.1109/CW.2018.00043
M3 - Conference contribution
AN - SCOPUS:85061441694
T3 - Proceedings - 2018 International Conference on Cyberworlds, CW 2018
SP - 184
EP - 190
BT - Proceedings - 2018 International Conference on Cyberworlds, CW 2018
A2 - Sourin, Alexei
A2 - Sourina, Olga
A2 - Erdt, Marius
A2 - Rosenberger, Christophe
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on Cyberworlds, CW 2018
Y2 - 3 October 2018 through 5 October 2018
ER -