Spiking Neural Networks for Classification of Brain-Computer Interface and Image Data

Spiking neural networks are a promising concept not only in terms of better simulation of biological neural networks but also in terms of overcoming the current disadvantages of artificial neural networks, such as high energy consumption or slow response time. The paper focuses on the potential benefits of spiking neural networks in the classification of event-related components processed in many traditional brain-computer interface experiments. Experiments with various spiking network architectures and optimization approaches over specific brain computer interface and image datasets are presented, and their results are provided and discussed. The best accuracy achieved was 64.86% for the event-related component dataset and 97.09% for the image dataset.