In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang et al. developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance.

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 1-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.

This article presents the method that won the brain-computer interface (BCI) competition IV addressed to the prediction of the finger flexion from electrocorticogram (ECoG) signals. ECoG-based BCIs have recently drawn the attention from the community. Indeed, ECoG can provide higher spatial resolution and better signal quality than classical EEG recordings. It is also more suitable for long-term use. These characteristics allow to decode precise brain activities and to realize efficient ECoG-based neuroprostheses. Signal processing is a very important task in BCIs research for translating brain signals into commands. Here, we present a linear regression method based on the amplitude modulation of band-specific ECoG including a short-term memory for individual finger flexion prediction. The effectiveness of the method was proven by achieving the highest value of correlation coefficient between the predicted and recorded finger flexion values on data set 4 during the BCI competition IV.

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The rapid development of remote sensing technology has brought abundant data support for deep learning based temperature forecasting research. However, recently proposed methods usually focus on the temporal relationship among temperature observation information, whereas ignore the spatial positions of different regions. Motivated by the observation that adjacent regions usually present similar temperature trends, in this paper we consider the temperature forecasting as a spatiotemporal sequence prediction problem, and propose a new deep learning model for temperature forecasting, Self-Attention Joint Spatiotemporal Network (SA-JSTN), which simultaneously captures the spatiotemporal interdependency information. The kernel component of the SA-JSTN is a newly developed Spatiotemporal Memory (STM) unit, which describes the temporal and spatial models via a unified memory cell. STM is constructed based on the units of the convolutional LSTM (ConvLSTM). Instead of using simple convolutions for spatial information extraction, in STM we improve ConvLSTM by a self-attention module, which has significantly enhanced the global spatial information representation ability of our proposed network. Compared with other deep learning based temperature forecasting methods, SA-JSTN is able to integrate the global spatial correlation into the temperature series prediction problem, and thus present better performance especially in short-term prediction. We have conducted comparison experiments on two typical temperature data sets to validate the effectiveness of our proposed method.