Mental Tasks Classification Based on Growing Selfª²Organizing Map
Liu Hailong,Wang Jue,Zheng Chongxun
£¨Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an
Jiaotong University, Xi'an 710049, China£©
Abstract£ºThe growing selfª²organizing map (Growing SOM) was adopted to perform mental
tasks classification in EEGª²based brainª²computer interface (BCI). The Growing SOM is
endowed with some capacities to outperform: Adapting the topological shape of the mapping
network according to the inherent structures of the input data to reflect the features of
the data distribution; Focusing on those mapping units with greater expression errors to
decrease the whole expression error of the mapping network to result in the more accurate
representation of the input data; Emphasizing the expression of the regions between
different classes of data to obviously improve the classification performances. Compared
with the traditional SOM, the Growing SOM with an accuracy of higher than 80£¥ in mental
tasks classification demonstrates the great potential applicability in BCI systems.
Keywords£ºbrainª²computer interface; electroencephalography; mental tasks
classification; growª²ing selfª²organizing map