Classification of mental tasks from EEG signals using extreme learning machine.
In this paper, a recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) is used to classify five mental tasks from different subjects using electroencephalogram (EEG) signals available from a well-known database. Performance of ELM is compared in terms of training time and classification accuracy with a Backpropagation Neural Network (BPNN) classifier and also Support Vector Machines (SVMs). For SVMs, the comparisons have been made for both 1-against-1 and 1-against-all methods. Results show that ELM needs an order of magnitude less training time compared with SVMs and two orders of magnitude less compared with BPNN. The classification accuracy of ELM is similar to that of SVMs and BPNN. The study showed that smoothing of the classifiers' outputs can significantly improve their classification accuracies.
Liang
Nan-Ying
NY
School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore. lny@pmail.ntu.edu.sg
Saratchandran
Paramasivan
P
Huang
Guang-Bin
GB
Sundararajan
Narasimhan
N
D000465:Algorithms; D004569:Electroencephalography / Q000145:classification / Q000379:methods; D006801:Humans; D007858:Learning / Q000502:physiology*; D008606:Mental Processes / Q000502:physiology*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors