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Description
Emotion classification using EEG signals has the potential of improving the social integration of patients suffering from neurological disorders such as Amyotrophic Lateral Sclerosis. The wearability of such classifiers requires the use of low-power hardware accelerators that would enable human-time interaction and extended operation periods. In this presentation, we describe the first hardware architecture of a neuromorphic processor for emotion classification using a pre-trained Convolutional Neural Network that uses pre-processed EEG signals. The data is recorded with a low-cost, off-the-shelf-device, and the classifier trained with two well-known datasets using Google’s TensorFlow. The classifier hardware is tested using the Atlys board.
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