 To accomplish this, we propose a multimodal approach that uses two different deep encoders, one for images and one for EEGs, trained in a siamese configuration  for learning a joint manifold that maximizes a compatibility measure between visual features and brain representation.
<fluency>  To accomplish this, we propose a multimodal approach that uses two different deep encoders, one for images and one for EEGs, trained in a siamese configuration  for learning a joint manifold that maximizes a compatibility measure between visual features and brain representation.
 To accomplish this, we propose a multimodal approach that uses two different deep encoders, one for images and one for EEGs, trained in a siamese configuration , for learning a joint manifold that maximizes a compatibility measure between visual features and brain representation.