@inproceedings{scholars7110,
           pages = {138--144},
           title = {EEG based pattern recognition method for classification of different mental tasking: Preliminary study for stroke survivors in Indonesia},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {Proceedings of the 2015 International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology, ICACOMIT 2015},
             doi = {10.1109/ICACOMIT.2015.7440193},
            year = {2016},
            note = {cited By 2; Conference of International Conference on Automation, Cognitive Science, Optics, Micro Electro-Mechanical System, and Information Technology, ICACOMIT 2015 ; Conference Date: 29 October 2015 Through 30 October 2015; Conference Code:121121},
        abstract = {This paper presents a result of pattern recognition method for different mental tasking of 8 volunteers. The EEG data used in this paper were acquired from 8 Indonesian volunteer using Emotiv EEG device with 16 channels. 24 feature extraction methods including time-domain and statistical features are applied to the EEG signal. An artificial neural network (ANN) is employed for classification. This is the preliminary study in developing a pattern recognition method for body prosthetic and wheelchair to disability person. The paper aims to investigate the reliable EEG signal from 14 channels. The results show that among 14 EEG channels, channel F7 and F8 are better in classification than other channels. The test classification of channel F7 and F8 is 66.7. {\^A}{\copyright} 2015 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84966667947&doi=10.1109\%2fICACOMIT.2015.7440193&partnerID=40&md5=550e3979d14ad5d63f2d17bbf443c54c},
        keywords = {Classification (of information); Extraction; Feature extraction; Mechanics; MEMS; Neural networks; Pattern recognition; Time domain analysis, Eeg datum; EEG signals; Feature extraction methods; Indonesia; Pattern recognition method; Statistical features; Stroke survivors; Time domain, Biomedical signal processing},
            isbn = {9781467374088},
          author = {Caesarendra, W. and Ariyanto, M. and Lexon, S. U. and Pasmanasari, E. D. and Chang, C. R. and Setiawan, J. D.}
}