Brain Computer Interfaces: A Recurrent Neural Network Approach

This paper explores the use of recurrent neural networks in the field of Brain Computer Interfaces(BCI). In particular it looks at a recurrent neural network, an echostate network and a CasPer neural network and attempts to use them to classify data from BCI competition IIIs dataset IVa. In addition it proposes a new method, EchoCasPer, which uses the CasPer training scheme in a recurrent neural network. The results showed that temporal information existed within the BCI data to be made use of, but further pre-processing and parameter exploration was needed to reach competitive classification rates.

[1]  Tamás D. Gedeon,et al.  Extending and Benchmarking the CasPer Algorithm , 1997, Australian Joint Conference on Artificial Intelligence.

[2]  Fusheng Yang,et al.  BCI competition 2003-data set IV:An algorithm based on CSSD and FDA for classifying single-trial EEG , 2004, IEEE Transactions on Biomedical Engineering.

[3]  Cuntai Guan,et al.  Expectation-Maximization Method for EEG-Based Continuous Cursor Control , 2007, EURASIP J. Adv. Signal Process..

[4]  José Carlos Príncipe,et al.  Analysis and Design of Echo State Networks , 2007, Neural Computation.

[5]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[6]  Tamás D. Gedeon,et al.  A Cascade Network Algorithm Employing Progressive RPROP , 1997, IWANN.

[7]  G. Pfurtscheller,et al.  The BCI competition III: validating alternative approaches to actual BCI problems , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.