  Practical brain-machine interfaces have been widely studied to accurately
detect human intentions using brain signals in the real world. However, the
electroencephalography (EEG) signals are distorted owing to the artifacts such
as walking and head movement, so brain signals may be large in amplitude rather
than desired EEG signals. Due to these artifacts, detecting accurately human
intention in the mobile environment is challenging. In this paper, we proposed
the reconstruction framework based on generative adversarial networks using the
event-related potentials (ERP) during walking. We used a pre-trained
convolutional encoder to represent latent variables and reconstructed ERP
through the generative model which shape similar to the opposite of encoder.
Finally, the ERP was classified using the discriminative model to demonstrate
the validity of our proposed framework. As a result, the reconstructed signals
had important components such as N200 and P300 similar to ERP during standing.
The accuracy of reconstructed EEG was similar to raw noisy EEG signals during
walking. The signal-to-noise ratio of reconstructed EEG was significantly
increased as 1.3. The loss of the generative model was 0.6301, which is
comparatively low, which means training generative model had high performance.
The reconstructed ERP consequentially showed an improvement in classification
performance during walking through the effects of noise reduction. The proposed
framework could help recognize human intention based on the brain-machine
interface even in the mobile environment.
