Reconstructing Static Memories from the Brain with EEG Feature Extraction and Generative Adversarial Networks


  • Matthew Zhang Westlake High School
  • Jeremy Lu Saratoga High School



Convolutional Neural Networks, General Adversarial Networks, EEG, Image Reconstruction, Machine Learning


Forensic tools and facial recognition techniques have implemented state memory reconstruction in the court of law. Recent research has incorporated machine learning into this field in order to fulfill the rising demand for this service. However, the suggested solutions require expensive cutting-edge equipment such as fMRI and CT scanners. The goal of the present study is to recreate images using user electroencephalography (EEG) input patterns. We combined a discriminator and generator network in order to decrease blur, face distortion, and erroneous features in the ImageNet dataset. An EEG feature matrix was produced using a convolutional neural network (CNN) encoder from spectrogram inputs, which are visual representations of the spectrum of electrical frequencies observed at each electrode. The encoder produced an 85% accuracy when linking spectrograms to the labels of the relevant images. The feature matrix was passed into the GAN during training. The viability of image reconstruction and coherent image representation from the brain were both proved feasible to a dependable degree due to our GAN output images’ resemblance to the original dataset. The present work could find applications in future studies as a non-invasive, more affordable option to recreate memories from brain signals.


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How to Cite

Zhang, M., & Lu, J. (2023). Reconstructing Static Memories from the Brain with EEG Feature Extraction and Generative Adversarial Networks. Journal of Student Research, 12(1).



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