  The new perspective in visual classification aims to decode the feature
representation of visual objects from human brain activities. Recording
electroencephalogram (EEG) from the brain cortex has been seen as a prevalent
approach to understand the cognition process of an image classification task.
In this study, we proposed a deep learning framework guided by the visual
evoked potentials, called the Event-Related Potential (ERP)-Long short-term
memory (LSTM) framework, extracted by EEG signals for visual classification. In
specific, we first extracted the ERP sequences from multiple EEG channels to
response image stimuli-related information. Then, we trained an LSTM network to
learn the feature representation space of visual objects for classification. In
the experiment, 10 subjects were recorded by over 50,000 EEG trials from an
image dataset with 6 categories, including a total of 72 exemplars. Our results
showed that our proposed ERP-LSTM framework could achieve classification
accuracies of cross-subject of 66.81% and 27.08% for categories (6 classes) and
exemplars (72 classes), respectively. Our results outperformed that of using
the existing visual classification frameworks, by improving classification
accuracies in the range of 12.62% - 53.99%. Our findings suggested that
decoding visual evoked potentials from EEG signals is an effective strategy to
learn discriminative brain representations for visual classification.
