Scene classification is a fundamental problem to understand the high-resolution remote sensing imagery. Recently, convolutional neural network (ConvNet) has achieved remarkable performance in different tasks, and significant efforts have been made to develop various representations for satellite image scene classification. In this paper, we present a novel representation based on a ConvNet with context aggregation. The proposed two-pathway ResNet (ResNet-TP) architecture adopts the ResNet [1] as backbone, and the two pathways allow the network to model both local details and regional context. The ResNet-TP based representation is generated by global average pooling on the last convolutional layers from both pathways. Experiments on two scene classification datasets, UCM Land Use and NWPU-RESISC45, show that the proposed mechanism achieves promising improvements over state-ofthe-art methods.
Electrodes connection on the scalp needs to apply gel or paste on the scalp and fit EEG-cap on the head and this procedure also needs to deal with the hair on it. By comparison, to fit EEG electrodes on the forehead area is much easier because there is no hair on it. If correlations of the EEGs generated from the forehead area are high with respect to EEGs from the sensorimotor area, it is then possible to achieve relatively high classification accuracy in motor imagery tasks just using EEG from forehead channels. In this way, it will help to make the procedure of motor imagery tasks much easier and convenient. Because correlation coefficients is often used to measure the similarity of two signals, it is necessary to study whether there is a high correlation between forehead channels' EEG and EEG from sensorimotor area during MI(Motor Imagery) tasks. In this paper, EEG data from three subjects were used in the tests. Firstly, a test was conducted on the correlation between EEGs from forehead 8(Fp1-Af8) channels and EEGs from the sensorimotor area channel C3, C4 during the MI tasks. The correlation is calculated with respect to ERP (Event-Related-Potential), Spectral Power between 6-25Hz, and ERSP (Event-Related-Spectral-Perturbation) at Alpha rhythms 8-12Hz. The results of the correlation tests are mostly above 70%. In particular, for subject Sl1, the correlation coefficient of ERSP between forehead channels and C3, C4 are as high as 0.9 during the left hand movement imagery trials. Secondly, we did a test on the classification of imagined left/right hand movement tasks using EEGs from 8 electrodes in the forehead. Classification results show that the accuracy of the forehead 8 channels' EEGs are as high as 81% for subject Sk6 comparing to 90% using 29 channels' EEG signal neighboring to C3, C4. For subject Sk3 and subject Sl1, the accuracies are 65% and 79% comparing to 80% and 83% using EEG signals from 29 channels neighboring to C3, C4. So there are high correlation between EEGs from the forehead area and EEGs from the sensorimotor area. That is to say, we can use EEGs from forehead in some situations, such as classification of left/right imagery, because they are much easier to measure than EEG form the sensorimotor area. This will make BCI system more portable and more convenient to use.
The concept of Brain-Computer Interface (BCI) has emerged over the last three decades as a promising alternative to the existing interface methods. However the BCI framework generally spoken only emphasizes on the aspects of BCI signal processing, lacking of the function of Visualization and Virtual Reality (VR) feedback. This paper designs a general and extendable framework which has the ability of offline, online analysis, visualization, and VR feedback. For the researchers, they can use it to analyze the online EEG signals, and observe the dynamic brain information of subjects. Meanwhile, the researchers can also do the offline analysis. For subjects, VR technology can provide a more secure and realistic environment for training and tuning neutrally controlled interfaces to real-world devices, such as wheelchairs. At last, the methods and algorithms used in the framework are also described.
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