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Hand gesture, a common non-verbal language, is being studied for Human Computer Interaction. Hand gestures can be categorized as static hand gestures and dynamic hand gestures. In recent years, effective approaches have been applied to hand gesture recognition.

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Speech production involves the synchronization of neural activity between the speech centers of the brain and the oralmotor system, allowing for the conversion of thoughts into

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One of the common modalities for observing mental activity is electroencephalogram (EEG) signals. However, EEG recording is highly susceptible to various sources of noise and to inter-subject differences. In order to solve these problems, we present a deep recurrent neural network (RNN) architecture to learn robust features and predict the levels of the cognitive load from EEG recordings. Using a deep learning approach, we first transform the EEG time series into a sequence of multispectral images which carries spatial information.

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Emotion recognition based on electroencephalography (EEG) has received attention as a way to implement human-centric
services. However, there is still much room for improvement, particularly in terms of the recognition accuracy. In this paper, we propose a novel deep learning approach using convolutional neural networks (CNNs) for EEG-based emotion recognition. In particular, we employ brain connectivity features that have not been used with deep learning models in

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