Deep Learning for Natural Image Reconstruction from Electrocorticography Signals Several recent studies proposed various methods for reconstructing natural images from human functional magnetic resonance imaging (fMRI) data. However, few studies have proposed reconstruction methods for electrophysiolgical brain activities such as electroencephalography (EEG) and electrocorticography (ECoG). To investigate whether natural images can be reconstructed from electrophysiological brain activities, we conducted a largescale experiment on natural image reconstruction from ECoG signals using deep learning. We first recorded ECoG signals from two macaque monkeys while presenting diverse natural images. Then, we trained several deep learning models for reconstructing presented images from ECoG signals. Comparing reconstruction models, we find that models trained with an adversarial loss produced reconstructions that contain visible features in presented images. Furthermore, our results with downsampled ECoG signals show the importance of rich temporal dynamics in ECoG signals for image reconstruction. Our results indicate the possibility of reconstructing diverse natural images from electrophysiological brain activities using deep learning.

Deep Learning in Physiological Signal Data: A Survey Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deeplearning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deeplearning approach that we review are the input data type, deeplearning task, deeplearning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deeplearning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deeplearning concept perspective such as training architecture and dataset sources.