An Evoked Potential-Guided Deep Learning Brain Representation For Visual Classification
This work addresses visual classification for neuroscience and AI applications by improving brain-computer interface decoding, though it is incremental as it builds on existing EEG and deep learning methods.
The study tackled the problem of decoding visual object feature representations from human brain activities using EEG signals, proposing an ERP-LSTM framework that achieved cross-subject classification accuracies of 66.81% for 6 categories and 27.08% for 72 exemplars, outperforming existing methods by 12.62% to 53.99%.
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.