Determining mental state from EEG signals using parallel implementations of neural networks
Scientific Programming - On applications analysis
Local Neural Classifier for EEG-Based Recognition of Mental Tasks
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
| Hi-index | 0.00 |
FFT and Multilayer neural networks (MLNN) have been applied to `Brain Computer Interface' (BCI). In this paper, in order to extract features of mental tasks, individual feature of brain waves of each channel is emphasized. Since the brain wave in some interval can be regarded as a vector, Gram-Schmidt orthogonalization is applied for this purpose. There exists degree of freedom in the channel order to be orthogonalized. Effect of the channel order on classification accuracy is investigated. Next, two channel orders are used for generating the MLNN input data. Two kinds of methods using a single NN and double NNs are examined. Furthermore, a generalization method, adding small random numbers to the MLNN input data, is applied. Simulations are carried out by using the brain waves, available from the Colorado State University website. By using the orthogonal components, a correct classification rate Pccan be improved from 70% to 78%, an incorrect classification rate Pecan be suppressed from 10% to 8%. As a result, a rate Rc= Pc/(Pc+ Pe) can be improved from 0.875 to 0.907. When two different channel orders are used, Pecan be drastically suppressed from 10% to 2%, and Rccan be improved up to 0.973. The generalization method is useful especially for using a sigle channel order. Pccan be increased up to 84~88% and Pecan be suppressed down to 2~4%, resulting in Rc= 0.957~0.977.