publication . Article . Other literature type . 2018

A New Regularized Matrix Discriminant Analysis (R-MDA) Enabled Human-Centered EEG Monitoring Systems

Jie Su; Linbo Qing; Xiaohai He; Hang Zhang; Jing Zhou; Yonghong Peng;
Open Access English
  • Published: 08 Feb 2018
  • Publisher: IEEE
  • Country: United Kingdom
Abstract
The wider use of wearable devices for electroencephalogram (EEG) data capturing provides a very useful way for the monitoring and self-management of human health. However, the large volumes of data with high dimensions cause computational complexity in EEG data processing and pose a great challenge to the use of wearable EEG devices in healthcare. This paper proposes a new approach to extract the structural information of EEG data and tackle the curse of dimensionality of the EEG data. A set of methods for dimensionality reduction (DR)-like linear discriminant analysis (LDA) and their improved methods have been developed for EEG processing in the literature. How...
Subjects
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: Data_Science, General Engineering, General Materials Science, General Computer Science, Distributed computing, Curse of dimensionality, Electroencephalography, medicine.diagnostic_test, medicine, Wearable technology, business.industry, business, Computational complexity theory, Computer science, Matrix decomposition, Pattern recognition, Artificial intelligence, Projection (linear algebra), Linear discriminant analysis, Dimensionality reduction
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25 references, page 1 of 2

[1] A. J. Casson, S. Smith, J. S. Duncan, et al, "Wearable EEG: what is it, why is it needed and what does it entail?," in Engineering in Medicine and Biology Society (EMBS), 30th Annual International Conference of the IEEE. IEEE, 2008

[2] A. J. Casson, D. C. Yates, S. J. M. Smith, et al, "Wearable electroencephalography," IEEE Eng. Med. Biol. Mag., vol. 29, no. 3, pp. 44-56, May, 2010, DOI: 10.1109/MEMB.2010.936545.

[3] L.-D. Liao, C.-Y. Chen, I.-J. Wang, et al, "Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors," J. Neuroeng. Rehabil., vol. 9, no. 1, Jan., 2012, DOI: 10.1186/1743-0003-9-5.

[4] B. K. Karumuri, A. Gupta, R. Liu, et al, "Classification of pre-ictal and interictal periods based on EEG frequency features in epilepsy," in Biomedical Engineering Conference (SBEC), 2016 32nd Southern. IEEE, 2016

[5] M. Murugappan, R. Nagarajan, S. Yaacob, "Comparison of different wavelet features from EEG signals for classifying human emotions," in Industrial Electronics and Applications (ISIEA), 2009 IEEE Symposium on. IEEE, 2009 [OpenAIRE]

[6] A. Bhardwaj, I. Vlachos, P. Jain, et al, "Classification of human emotions from EEG signals using SVM and LDA classifiers," in Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on. IEEE, 2015

[7] S.-L. Wu, Y.-T. Liu, K.-P. Chou, et al, "A motor imagery based braincomputer interface system via swarm-optimized fuzzy integral and its application," in Fuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference on. IEEE, 2016

[8] C.-T. Lin, C.-H. Chuang, C.-S. Huang, et al, "Wireless and wearable EEG system for evaluating driver vigilance," IEEE Trans. Biomed. Circuits Syst., vol. 8, no. 2, pp. 165-176, May, 2014, DOI: 10.1109/TBCAS.2014.2316224.

[9] R. Xu, N. Jiang, C. Lin, et al, "Enhanced low-latency detection of motor intention from EEG for closed-Loop brain-computer interface applications," IEEE Trans. Biomed. Eng., vol. 61, no. 2, pp. 288-296, Feb., 2014, DOI: 10.1109/TBME.2013.2294203.

[10] W. He, Y. Zhao, H.-Y Tang , et al, "A wireless BCI and BMI system for wearable robots," IEEE Trans. Syst. Man Cybern., Syst., vol. 46, no. 7, pp. 936-946, Jul., 2016, DOI: 10.1109/TSMC.2015.2506618 .

[11] H. Zhou and L.-X. Li, "Regularized matrix regression," J. R. Stat. Soc. Series B Stat. Methodol., vol. 76, no. 2, pp. 463-483, Mar., 2014, DOI: 10.1111/rssb.12031.

[12] L. Luo, Y.-B. Xie, Z.-H. Zhang, et al, "Support matrix machines," in International Conference on Machine Learning (ICML), ICML'15 Proceedings of the 32nd International Conference on. PMLR, 2015

[13] R. O. Duda, P. E. Hart, D. G. Stork, "Fisher linear discriminant," in Pattern classification, 2nd ed. New York, NY, USA: John Wiley & Sons, 2001.

[14] A. Subasi, M. I. Gursoy, "EEG signal classification using PCA, ICA, LDA and support vector machines," Expert Syst. Appl., vol. 37, no. 12, pp. 8659- 8666, Dec., 2010, DOI: 10.1016/j.eswa.2010.06.065. [OpenAIRE]

[15] J.-P. Ye, R. Janardan, Q. Li, "Two-dimensional linear discriminant analysis," in Advances in Neural Information Processing Systems 17 (NIPS), Neural Information Processing Systems. DBLP, 2004

25 references, page 1 of 2
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