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Recently, deep neural networks (DNNs) have shown the remarkable success of feature representations in computer vision, audio analysis, and natural language processing. Furthermore, DNNs have been used for electroencephalography (EEG) signal classification in recent studies on brain–computer interface. However, most works use one-dimensional EEG features to learn DNNs that ignores the local information within multichannel or multiple frequency bands in the EEG signals. In this paper, we propose a novel emotion recognition method using a convolutional neural network (CNN) while preventing the loss of local information. The proposed method consists of two parts. The first part generates topology-preserving differential entropy features while keeping the distance from the center electrode to other electrodes. The second part learns the proposed CNN to estimate three-class emotional states (positive, neutral, negative). We evaluate our work on SEED dataset, including 62-channel EEG signals recorded from 15 subjects. Our experimental results demonstrate that the proposed method achieved superior performance on SEED dataset with an average accuracy of 90.41% with the visualization of extracted features from the proposed CNN using t-SNE to show our representation outperforms the other representations based on standard features for EEG analysis. Besides, with the additional experiment on VIG dataset to estimate the vigilance of EEG dataset, we show the off-the-shelf availability of the proposed method. [ABSTRACT FROM AUTHOR]

Automatic gender classification in speech is a challenging research field with a wide range of applications in HCI (human-computer interaction). A couple of decades of research have shown promising results, but there is still a need for improvement. Until now, gender classification has been made using differences in the spectral characteristics of males and females. We assumed that a neutral margin exists between the male and female spectral range. This margin causes misclassification of gender. To address this limitation, we studied three non-lexical speech features (fillers, overlapping, and lengthening). From the statistical analysis, we found that overlapping and lengthening are effective in gender classification. Next, we performed gender classification using overlapping, lengthening, and the baseline acoustic feature, Mel Frequency Cepstral Coefficient (MFCC). We have tried to achieve the best results by using various combinations of features at the same time or sequentially. We used two types of machine-learning methods, support vector machine (SVM) and recurrent neural networks (RNN), to classify the gender. We achieved 89.61% with RNN using a feature set including MFCC, overlapping, and lengthening at the same time. Also, we have reclassified using non-lexical features with only data belonging to the neutral margin which was empirically selected based on the result of gender classification with only MFCC. As a result, we determined that the accuracy of classification with RNN using lengthening was 1.83% better than when MFCC alone was used. We concluded that new speech features could be effective in improving gender classification through a behavioral approach, notably including emergency calls. [ABSTRACT FROM AUTHOR]