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

2019/05/10 by Xiang Zhang, Zhang, Xiang, Lina Yao +11 · 1 voice · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #EEG and Brain-Computer Interfaces #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Functional Brain Connectivity Studies #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Neurons and Cognition (q-bio.NC) #Signal Processing (eess.SP) #cs.HC #cs.LG #eess.SP #electronic engineering #information engineering #q-bio.NC

paper · pdf · doi:10.48550/arxiv.1905.04149

openalex publication_date 2019/05/10 · arxiv published 2019/05/10 · arxiv updated 2020/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks
• Deep learning in neural networks: An overview
• Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials
• Motor imagery and direct brain-computer communication
• Representation Learning: A Review and New Perspectives
• Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks
• Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
• Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• A deep learning architecture for temporal sleep stage classification\n using multivariate and multimodal time series

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Discussions

• A Survey on Deep Learning Based Brain Computer Interface [hn, 3 points, 0 comments]

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