Journal ArticleDOI
An Attention-based Bi-LSTM Method for Visual Object Classification via EEG
Xiao Zheng,Wanzhong Chen +1 more
TLDR
The experimental results not only could provide strong support for the modularity theory about the brain cognitive function, but show the superiority of the proposed Bi-LSTM model with attention mechanism again.About:
This article is published in Biomedical Signal Processing and Control.The article was published on 2021-01-01. It has received 50 citations till now. The article focuses on the topics: Visual perception & Feature (machine learning).read more
Citations
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Journal ArticleDOI
A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction
Babak Alizadeh,Alireza Ghaderi Bafti,Hamid Kamangir,Yu Zhang,Daniel B. Wright,Kristie J. Franz +5 more
TL;DR: The novel SAINA-LSTM model outperforms other models in low, medium, and high ranges of forecasts and for 1- to 7-day ahead forecasts in all three highly nonlinear and non-snow-driven study basins.
Journal ArticleDOI
A Novel DE-CNN-BiLSTM Multi-Fusion Model for EEG Emotion Recognition
TL;DR: This research presents a novel model named DE-CNN-BiLSTM deeply integrating the complexity of EEG signals, the spatial structure of brain and temporal contexts of emotion formation, and the new model can decode the EEG signal deeply and extract key emotional features to improve accuracy.
Journal ArticleDOI
Novel double layer BiLSTM minor soft fault detection for sensors in air-conditioning system with KPCA reducing dimensions
TL;DR: It can be seen from the results that KPCA-DL-BiLSTM had better fault detection accuracy and stability for minor soft faults, especially for drift deviation faults.
Journal ArticleDOI
Towards Efficient and Intelligent Internet of Things Search Engine
TL;DR: In this paper, a generic framework for the IoT search engine is proposed, and a naming service for the system is presented, which is an essential component for an effective search engine.
Journal ArticleDOI
EEG-ConvTransformer for single-trial EEG-based visual stimulus classification
TL;DR: In this paper , an EEG-ConvTranformer network that is based on both multi-headed self-attention and temporal convolution is proposed to capture inter-region interaction patterns.
References
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Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Journal ArticleDOI
LSTM: A Search Space Odyssey
TL;DR: This paper presents the first large-scale analysis of eight LSTM variants on three representative tasks: speech recognition, handwriting recognition, and polyphonic music modeling, and observes that the studied hyperparameters are virtually independent and derive guidelines for their efficient adjustment.
Electroencephalography: Basic Principles, Clinical Applications and Related Fields, Fourth Edition
TL;DR: Historical aspects introduction to the neurophysiological basis of the EEG and DC potentials cellular substrates of spontaneous and evoked brain rhythms dynamics of EEG as signals and neuronal populations are introduced.
Book
Electroencephalography: Basic Principles, Clinical Applications, and Related Fields
TL;DR: The main thrust of Electroencephalography is to preserve the sound basis of classic EEG recording and reading and, on the other hand, to present the newest developments for future EEG/neurophysiology research, especially in view of the highest brain functions as mentioned in this paper.