Intent Recognition in Smart Living Through Deep Recurrent Neural Networks

@inproceedings{Zhang2017IntentRI,
  title={Intent Recognition in Smart Living Through Deep Recurrent Neural Networks},
  author={X. Zhang and Lina Yao and Chaoran Huang and Quan Z. Sheng and Xianzhi Wang},
  booktitle={ICONIP},
  year={2017}
}
Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer world. However, the utilization of EEG signals is challenged by low accuracy, arduous and time-consuming feature extraction. This paper proposes a 7-layer deep learning model to classify raw EEG signals with the aim of recognizing subjects’ intents, to avoid the… Expand
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References

SHOWING 1-10 OF 25 REFERENCES
A novel deep learning approach for classification of EEG motor imagery signals.
TLDR
The results show that deep learning methods provide better classification performance compared to other state of art approaches and can be applied successfully to BCI systems where the amount of data is large due to daily recording. Expand
A Deep Learning Method for Classification of EEG Data Based on Motor Imagery
TLDR
The performance of the proposed DBN was tested with different combinations of hidden units and hidden layers on multiple subjects, the experimental results showed that the proposed method performs better with 8 hidden layers, and there was an improvement of 4 – 6% for certain cases. Expand
Shrinkage estimator based regularization for EEG motor imagery classification
TLDR
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
CLASSIFICATION OF WAVELET TRANSFORMED EEG SIGNALS WITH NEURAL NETWORK FOR IMAGINED MENTAL AND MOTOR TASKS
TLDR
EEG signals are transformed by means of discrete wavelet transform and the obtained signal features are used as inputs for a neural network classifier that should separate five different sets of EEG signals representing various mental tasks. Expand
EEG Mouse:A Machine Learning-Based Brain Computer Interface
TLDR
The main idea of the current work is to use a wireless Electroencephalography headset as a remote control for the mouse cursor of a personal computer using EEG signals as a communication link between brains and computers. Expand
A Flexible Multichannel EEG Feature Extractor and Classifier for Seizure Detection
TLDR
A low-power, flexible, and multichannel electroencephalography (EEG) feature extractor and classifier for the purpose of personalized seizure detection found that five features per channel with logistic regression proved to be the best solution. Expand
Applications of UBMs and I-vectors in EEG subject verification
  • Christian R. Ward, J. Picone, I. Obeid
  • Computer Science, Medicine
  • 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • 2016
TLDR
The initial results support the development of I-vectors and Joint Factor Analysis as a viable approach to addressing subject verification within and across subjects. Expand
Information technologies Design of Smart Home System Using EEG Signal
In order to convenience of the disabled people living more convenient, using EEG signal is a good way, this paper design a smart home system based on the results of EEG signal studies, through thisExpand
Enhanced Living by Assessing Voice Pathology Using a Co-Occurrence Matrix
TLDR
The results demonstrate the feasibility of the proposed voice pathology assessment system in light of its high accuracy and speed and can be extended to assess other disabilities in an ELE. Expand
Classification of imagery motor EEG data with wavelet denoising and features selection
  • Lei Sun, Zu Ren Feng
  • Computer Science
  • 2016 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)
  • 2016
TLDR
Experimental results show that the classification accuracy of the proposed method is significantly improved compared to the same PSD feature selection method without wavelet denoising, and certainly indicated that wavelet Denoising algorithm successfully purified motor imagery EEG data and made classifying features more prominent. Expand
...
1
2
3
...