Mental Task Recognition by EEG Signals: A Novel Approach with ROC Analysis

Electroencephalogram or electroencephalography (EEG) has been widely used in medical fields and recently in cognitive science and brain-computer interface (BCI) research. To distinguish metal tasks such as reading, calculation, motor imagery, etc., it is generally to extract features of EEG signals by dimensionality reduction methods such as principle component analysis (PCA), linear determinant analysis (LDA), common spatial pattern (CSP), and so on for classifiers, for example, k-nearest neighbor method (kNN), kernel support vector machine (SVM), and artificial neural networks (ANN). In this chapter, a novel approach of feature extraction of EEG signals with receiver operating characteristic (ROC) analysis is introduced

Training Deep Neural Networks with Reinforcement Learning for Time Series Forecasting

As a kind of efficient nonlinear function approximators, artificial neural networks (ANN) have been popularly applied to time series forecasting. The training method of ANN usually utilizes error back-propagation (BP) which is a supervised learning algorithm proposed by Rumelhart et al. in 1986; meanwhile, authors proposed to improve the robustness of the ANN for unknown time series prediction using a reinforcement learning algorithm named stochastic gradient ascent (SGA) originally proposed by Kimura and Kobayashi for control problems in 1998. We also successfully use a deep belief net (DBN) stacked by multiple restricted Boltzmann machines (RBMs) to realized time series forecasting in 2012. In this chapter, a state-of-the-art time series forecasting system that combines RBMs and multilayer perceptron (MLP) and uses SGA training algorithm is introduced. Experiment results showed the high prediction precision of the novel system not only for benchmark data but also for real phenomenon time series data

Parameterless-Growing-SOM and Its Application to a Voice Instruction Learning System

An improved self-organizing map (SOM), parameterless-growing-SOM (PL-G-SOM), is proposed in this paper. To overcome problems existed in traditional SOM (Kohonen, 1982), kinds of structure-growing-SOMs or parameter-adjusting-SOMs have been invented and usually separately. Here, we combine the idea of growing SOMs (Bauer and Villmann, 1997; Dittenbach et al. 2000) and a parameterless SOM (Berglund and Sitte, 2006) together to be a novel SOM named PL-G-SOM to realize additional learning, optimal neighborhood preservation, and automatic tuning of parameters. The improved SOM is applied to construct a voice instruction learning system for partner robots adopting a simple reinforcement learning algorithm. User's instructions of voices are classified by the PL-G-SOM at first, then robots choose an expected action according to a stochastic policy. The policy is adjusted by the reward/punishment given by the user of the robot. A feeling map is also designed to express learning degrees of voice instructions. Learning and additional learning experiments used instructions in multiple languages including Japanese, English, Chinese, and Malaysian confirmed the effectiveness of our proposed system

Learning Petri Network and Its Application to Non-linear System Control

According to the recent knowledge of brain science, it is suggested that there exists the functions distribution in the brain, which means that different neurons are activated depending on which sort of sensory information the brain receives. We have already developed a learning network with functions distribution which is called Learning Petri Network (LPN) and have also shown that this network could learn nonlinear and discontinuous mappings which Neural Network(NN) can not. In this paper, a more realistic application which has dynamic characteristics is studied. From simulation results of a nonlinear crane control system using LPN controller, it has been proved that the control performance of LPN controller is superior to that of NN controller

Universal Learning Network and its Application Systems

Universal Learning Network(ULN) and its application to control systems are discussed. In ULN, any kinds of nonlinearly operated nodes with a continuously differentiable function are connected to each other by multi-branches that may have arbitrary time delays including zero or minus ones. A generalized learning algorithm is proposed, which can be applied to any kinds of networks including static or dynamic networks, feedforward or recurrent networks, time delay neural networks and networks with multi-branches. One of the most important features of ULN is the use of the higher order derivatives. As for the application of ULN, control problems such as robust control and chaotic control are studied using second order derivatives and it is shown that the second order derivatives are effective tools to realize the sophisticated robust control and chaotic control in the nonlinear systems