Journal
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Volume 16, Issue 1, Pages 29-38Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065706000482
Keywords
Brain Computer Interfaces (BCIs); electroencephalogram (EEG); mental task classification; Support Vector Machines (SVMs); Backpropagation Neural Networks (BPNNs); Extreme Learning Machine (ELM)
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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 I-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.
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