Proceedings ArticleDOI
Classification of motor imagery eeg using wavelet envelope analysis and LSTM networks
Jie Zhou,Ming Meng,Yunyuan Gao,Yuliang Ma,Qizhong Zhang +4 more
- pp 5600-5605
TLDR
A novel method based on wavelet envelope analysis and long-term short-term memory (LSTM) classifier which consider the amplitude modulation characteristics and time series information of MI-EEG to classify EEG signals into multiple classes is proposed.Abstract:
Motor imagery (MI) based brain-computer interface (BCI) facilitates a medium to translate the human motion intentions using Motor imagery electroencephalogram (EEG) into control signals. A major challenge in BCI research is the identification of non-stationary brain electrical signals to categorize human motion intentions. We propose a novel method based on wavelet envelope analysis and long-term short-term memory (LSTM) classifier which consider the amplitude modulation characteristics and time series information of MI-EEG to classify EEG signals into multiple classes. First, the Hilbert transform (HT) and discrete wavelet transform (DWT) are combined to extract significant features which contains the underlying information of both amplitude modulation and frequency modulation of the EEG signals. Then, the wavelet envelope features are input into an LSTM classifier with input gates, forget gates, and output gates for classification. Finally, the experiment was conducted on the 2003 BCI competition data set III with 5-fold cross-validation, and experimental results show that the proposed method helps achieve higher classification accuracy.read more
Citations
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Journal ArticleDOI
EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges.
TL;DR: In this article, state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used.
Journal ArticleDOI
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review
Mamunur Rashid,Norizam Sulaiman,Anwar P. P. Abdul Majeed,Rabiu Muazu Musa,Ahmad Fakhri Ab. Nasir,Bifta Sama Bari,Sabira Khatun +6 more
TL;DR: This article provides a comprehensive review of the state-of-the-art of a complete BCI system and a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics.
Journal ArticleDOI
A novel hybrid deep learning scheme for four-class motor imagery classification.
TL;DR: A hybrid deep network framework to improve classification accuracy of four-class MI-EEG signal is proposed and could be of great interest for real-life brain-computer interfaces (BCIs).
Proceedings ArticleDOI
Prediction of Depression from EEG Signal Using Long Short Term Memory(LSTM)
S.Dhananjay Kumar,DP Subha +1 more
TL;DR: LSTM (Long-short term memory) deep learning models are used in the prediction of trends of depression for the next time instants, based on the features extracted from EEG signals.
Journal ArticleDOI
Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network.
Stefano Tortora,Stefano Ghidoni,Carmelo Chisari,Silvestro Micera,Silvestro Micera,Fiorenzo Artoni +5 more
TL;DR: The results support for the first time the use of a memory-based deep learning classifier to decode walking activity from non-invasive brain recordings and suggest that this classifier can be a more effective input for devices restoring locomotion in impaired people.
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Journal ArticleDOI
Brain-computer interface technology: a review of the first international meeting
Jonathan R. Wolpaw,Niels Birbaumer,W.J. Heetderks,Dennis J. McFarland,Paul Hunter Peckham,Gerwin Schalk,Emanuel Donchin,L.A. Quatrano,C.J. Robinson,C.J. Robinson,Theresa M. Vaughan +10 more
TL;DR: The first international meeting devoted to brain-computer interface research and development is summarized, which focuses on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users.