Corpus ID: 3953804

Know Your Mind: Adaptive Brain Signal Classification with Reinforced Attentive Convolutional Neural Networks

  title={Know Your Mind: Adaptive Brain Signal Classification with Reinforced Attentive Convolutional Neural Networks},
  author={X. Zhang and Lina Yao and Xianzhi Wang and W. Zhang and Z. Yang and Yunhao Liu},
Electroencephalography (EEG) signals reflect activities on certain brain areas. Effective classification of time-varying EEG signals is still challenging. First, EEG signal processing and feature engineer- ing are time-consuming and highly rely on expert knowledge. In addition, most existing studies focus on domain-specific classifi- cation algorithms which may not be applicable to other domains. Moreover, the EEG signal usually has a low signal-to-noise ratio and can be easily corrupted. In… Expand
Deep learning-based electroencephalography analysis: a systematic review
This work reviews 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring, to extract trends and highlight interesting approaches in order to inform future research and formulate recommendations. Expand
EEG-based Depression Detection Using Convolutional Neural Network with Demographic Attention Mechanism
Experimental results showed that the proposed method is superior to the unitary 1-D CNN without gender and age factors and two other ways of incorporating demographics, and indicates that organic mixture of EEG signals and demographic factors is promising for the detection of depression. Expand
Acquisition , analysis and classification of EEG signals for control design
In the design of brain machine interfaces it is common to use motor imagery which is the mental simulation of a motor act, it consists of acquiring the signals emitted when imagining the movement ofExpand
Processing and Analysis of Signals with Superposed Noises by Artificial Neural Networks
It is established that the use of FFT in the signal processing is not achieve the desire effect for improvement of classification parameters. Expand
Feasibility Analysis of Tactical Radio Station Communication Behaviors Cognition
  • Huaji Zhou, Lifeng Yang, Zilong Wu
  • 2021 Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)
  • 2021
In the field of electronic countermeasures, it is extremely difficult to effectively identify communication behaviors of tactical radio station by analyzing communication protocols of theExpand


Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
A novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities is proposed, which outperforms a series of baselines and the competitive state-of-the-art methods. Expand
EEG-based Intention Recognition from Spatio-Temporal Representations via Cascade and Parallel Convolutional Recurrent Neural Networks
Both cascade and parallel convolutional recurrent neural network models for precisely identifying human intended movements by effectively learning compositional spatio-temporal representations of raw EEG streams are introduced. Expand
Improving EEG-Based Motor Imagery Classification via Spatial and Temporal Recurrent Neural Networks
This paper proposed a pure RNNs-based parallel method for encoding spatial and temporal sequential raw data with bidirectional Long Short- Term Memory (bi-LSTM) and standard LSTM, respectively, and demonstrated the superior performance of this approach in the multi-class trial-wise movement intention classification scenario. Expand
Shrinkage estimator based regularization for EEG motor imagery classification
A novel regularisation approach based on shrinkage estimation is presented in order to handle small sample problem and retain subject-specific discriminative features and results show that Shrinkage Regularized Filter Bank CSP (SR-FBCSP) outperforms FBCSP in classifying left vs right hand motor imagery. Expand
Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures
This approach delivers a sensitivity above 90% while maintaining a specificity below 5%. Expand
Intent Recognition in Smart Living Through Deep Recurrent Neural Networks
A 7-layer deep learning model is proposed to classify raw EEG signals with the aim of recognizing subjects’ intents, to avoid the time consumed in pre-processing and feature extraction. Expand
Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification
The proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification, and has correctly identified all the subjects and provided a statistically significant improvement of the identification accuracy. Expand
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
The ConvNets and visualization techniques used in this study constitute a next step towards clinically useful automated EEG diagnosis and establish a new baseline for future work on this topic. Expand
MindID: Person Identification from Brain Waves through Attention-based Recurrent Neural Network
MindID, an EEG-based biometric identification approach, achieves higher accuracy and better characteristics and has the potential to be largely deployment in practice environment. Expand
Improved EEG event classification using differential energy
A comparison of a variety of approaches to estimating and postprocessing features on the TUH EEG Corpus, and a variant of a standard filter bank-based approach, coupled with first and second derivatives, provides a substantial reduction in the overall error rate. Expand