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https://www.selleckchem.com/products/LY315920(Varespladib).html 001). In particular, RiSC improved HF difference the most for datasets with the most severe outlier contamination.EEG signal classification is an important task to build an accurate Brain Computer Interface (BCI) system. Many machine learning and deep learning approaches have been used to classify EEG signals. Besides, many studies have involved the time and frequency domain features to classify EEG signals. On the other hand, a very limited number of studies combine the spatial and temporal dimensions of the EEG signal. Brain dynamics are very complex across different mental tasks, thus it is difficult to design efficient algorithms with features based on prior knowledge. Therefore, in this study, we utilized the 2D AlexNet Convolutional Neural Network (CNN) to learn EEG features across different mental tasks without prior knowledge. First, this study adds spatial and temporal dimensions of EEG signals to a 2D EEG topographic map. Second, topographic maps at different time indices were cascaded to populate a 2D image for a given time window. Finally, the topographic maps enabled the AlexNet to learn features from the spatial and temporal dimensions of the brain signals. The classification performance was obtained by the proposed method on a multiclass dataset from BCI Competition IV dataset 2a. The proposed system obtained an average classification accuracy of 81.09%, outperforming the previous state-of-the-art methods by a margin of 4% for the same dataset. The results showed that converting the EEG classification problem from a (1D) time series to a (2D) image classification problem improves the classification accuracy for BCI systems. Also, our EEG topographic maps enabled CNN to learn subtle features from spatial and temporal dimensions, which better represent mental tasks than individual time or frequency domain features.In this paper, we review several advances in different fields that provide ne

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