Title :
Improving classification of EEG signals for a four-state brain machine interface
Author :
Hema, C.R. ; Paulraj, M.P. ; Adom, Abdul Hamid
Author_Institution :
Fac. of Eng., Karpagam Univ., Coimbatore, India
Abstract :
Neural network classifiers are one among the popular modes in the design of classifiers for electroencephalograph based brain machine interfaces. This study presents algorithms to improve the classification performance of motor imagery for a four state brain machine interface. Dynamic neural network models with band power and Parseval energy density features are proposed to improve the classification of task signals. Motor imagery signals recorded noninvasively at the sensorimotor cortex region using two bipolar electrodes are used in the study. The performances of the proposed algorithms are compared with a static neural classifier. Average classification performance of 97.7% was achievable. Experiment results show that the distributed time delay neural network model out performs the layered recurrent and feed forward neural classifiers.
Keywords :
biomedical electrodes; brain-computer interfaces; electroencephalography; handicapped aids; medical signal processing; neural nets; signal classification; EEG signals; Parseval energy density features; bipolar electrodes; classification performance; distributed time delay neural network model; dynamic neural network models; electroencephalography; feed forward neural classifiers; four-state brain machine interface; layered recurrent neural classifiers; motor imagery; neural network classifiers; sensorimotor cortex region; signal classification; static neural classifier; Band Power; Brain Machine Interfaces; Dynamic Neural Networks; Neural Networks; Parseval theorem;
Conference_Titel :
Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on
Conference_Location :
Langkawi
Print_ISBN :
978-1-4673-1664-4
DOI :
10.1109/IECBES.2012.6498042