A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers

Abstract

Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has lifted the performance of brain-computer interface systems significantly in recent years. In this article, we systematically investigate brain signal types for BCI and related deep learning concepts for brain signal analysis. We then present a comprehensive survey of deep learning techniques used for BCI, by summarizing over 230 contributions most published in the past five years. Finally, we discuss the applied areas, opening challenges, and future directions for deep learning-based BCI.

arXiv commentAccepted by Journal of Neural Engineering. Summarized more than 200+ brain signal-related papers, systematically covering 8 Brain-Computer Interface (BCI) categories and 10+ deep learning models

References in corpus (25)

• Deep Learning in Neural Networks: An Overview
• A Survey on Deep Learning in Medical Image Analysis
• Brain Tumor Segmentation with Deep Neural Networks
• DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
• Applications of Deep Learning and Reinforcement Learning to Biological Data
• A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
• Spatial-Temporal Recurrent Neural Network for Emotion Recognition
• Mixed Neural Network Approach for Temporal Sleep Stage Classification
• Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials
• Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks
• Feature Learning from Incomplete EEG with Denoising Autoencoder
• Using Riemannian geometry for SSVEP-based Brain Computer Interface
• SLEEPNET: Automated Sleep Staging System via Deep Learning
• Deep Feature Learning for EEG Recordings
• Deep learning of fMRI big data: a novel approach to subject-transfer decoding
• Mental State Recognition via Wearable EEG
• Deep Architectures for Automated Seizure Detection in Scalp EEGs
• Individual Recognition in Schizophrenia using Deep Learning Methods with Random Forest and Voting Classifiers: Insights from Resting State EEG Streams
• The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks
• DeepKey: An EEG and Gait Based Dual-Authentication System
• Deep Learning for Fatigue Estimation on the Basis of Multimodal Human-Machine Interactions
• Comparison of Training Methods for Deep Neural Networks
• Brain2Object: Printing Your Mind from Brain Signals with Spatial Correlation Embedding
• Information-Theoretic Analysis of Refractory Effects in the P300 Speller
• Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction

Cited by in corpus (6)

• Open Problems in Cooperative AI
• Know Your Mind: Adaptive Brain Signal Classification with Reinforced Attentive Convolutional Neural Networks
• Improving EEG Decoding via Clustering-based Multi-task Feature Learning
• Multi-Scale Neural network for EEG Representation Learning in BCI
• Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection
• Motor Imagery Classification Emphasizing Corresponding Frequency Domain Method based on Deep Learning Framework