Learning Representations from EEG with Deep Recurrent-Convolutional\n Neural Networks

One of the challenges in modeling cognitive events from electroencephalogram\n(EEG) data is finding representations that are invariant to inter- and\nintra-subject differences, as well as to inherent noise associated with such\ndata. Herein, we propose a novel approach for learning such representations\nfrom multi-channel EEG time-series, and demonstrate its advantages in the\ncontext of mental load classification task. First, we transform EEG activities\ninto a sequence of topology-preserving multi-spectral images, as opposed to\nstandard EEG analysis techniques that ignore such spatial information. Next, we\ntrain a deep recurrent-convolutional network inspired by state-of-the-art video\nclassification to…