S FIS 1005397 Bibliography
44
DAFTAR PUSTAKA
Aapo, Hyvarinen., Juha, Karhunen., Erkki, Oja. (2001). Independent Component
Analysis, John Wiley & Sons, inc: New York.
Abootalebi, Vahid., Moradi, M Hassan., Khalilzadeh, M Ali. (2009). A new
approach for EEG feature extraction in P300-based lie detection. Computer
methods and programs in biomedicine, vol.94 hlm.48-57.
Ahmed, K. S. (2011). Wheelchair movement control via human eye blinks.
American journal of biomedical engineering, vol.1 hlm.55-58.
Akhtar, M.T., Mitsuhashi, W., James, C.J. Employing spatially constrained ICA
and wavelet denoising, for automatic removal of artifacts from multichannel
EEG data. (2012). Signal Processing, vol.27 pp.401–416.
Akram, F., Han, H.-S., & Kim, T.-S. (2014). A P300-based brain computer
interface system for words typing. Computers in Biology and Medicine,
vol.45 hlm.118-125.
Amri, Moch. Choirul., Adil, Ratna. (t.t). Rancang Bangun Modul EEG Untuk
Pengkalisifikasikan Keaadaan Stress dengan FIR. Departemen Teknik
Elektronika, Politeknik Elektronika Negeri Surabaya.
Bi, Luzheng., Fan,Xin-An., Liu,Yili. (2013). EEG-Based Brain-Controlled Mobile
Robots: A Survey. IEEE Transactions ON Human-Machine Systems,
Vol. 43, No. 2, March 2013.
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B.,
Kubler, A., Perelmouter, J., Taub, E., Flor, H. (1999). "A Spelling
Device for the Paralysed". Nature, vol.398 hlm. 297-298.
Gourab, Krishnaj., Schmit, Brian D. (2010). Changes in movement-related b-band
EEG signals in human spinal cord injury. Clinical Neurophysiology,
vol.121 hlm.2017–2023.
Gunadi, Aris., Gede, I., Harjoko, Agus. (2012). Telaah Metode-Metode
Pendeteksi Kebohongan. IJCCS, vol.6 hlm.35-46.
Bagus Kumbara,2015
KLASIFIKASI DAN EKSTRAKSI SINYAL EEG-P300 MENGGUNAKAN SUPPORT VECTOR MACHINE
UNTUK DETEKSI KEBOHONGAN
Universitas Pendidikan Indonesia | repository.upi.edu | perpustakaan.upi.edu
45
Halchenko, Yaroslav O., Hanson, Stephen J., Pearlmutter, Barak A. (2005).
Multimodal Integration: fMRI, MRI, EEG, MEG. hlm223-265.
Bagus Kumbara,2015
KLASIFIKASI DAN EKSTRAKSI SINYAL EEG-P300 MENGGUNAKAN SUPPORT VECTOR MACHINE
UNTUK DETEKSI KEBOHONGAN
Universitas Pendidikan Indonesia | repository.upi.edu | perpustakaan.upi.edu
46
Hazrati, M. K., & Erfanian, A. (2010). An online EEG-based brain–computer
interface for controlling hand grasp using an adaptive probabilistic
neural network. Medical Engineering & Physics, vol.32 hlm.730-739.
Hsu, Wei-Yen. (2010). EEG-based motor imagery classification using neurofuzzy prediction and wavelet fractal features. Journal of Neuroscience
Methods, vol.189 hlm.295-302.
Hwang, Han-Jeong., lim, Jeong-Hwan., Jung, Young-jin., Choi, Han., Lee, Sang
Woo., Im, Chang-Hwan. (2012). Development of an SSVEP-based BCI
spelling system adopting a QWERTY-style LED keyboard. Journal of
Neuroscience Methods, vol.208 hlm.59–65.
Jahankhani, Pari., Kodogiannis, Vassilis., Revett, Kenneth. (2006). EEG signal
classification using wavelet feature extraction and neural networks.
IEEE John Vincent Atanasoff 2006 International Symposium on
Modern Computing.
Kalcher, J., Flotzinger, D., Neuper, Ch., Golly, S., Pfurtscheller, G. (1996). Graz
braincomputer interface II: towards communication between humans
and computers based on online classification of three different EEG
patterns. Medical and Biological Engineering and Computing, vol.34
hlm.382-388.
Karyawan, Moch A., Arifin, Agus Z., Saikhu, Ahmad. (2011). Klasisfikasi Sinyal
EEG Menggunakan Koefisien Autoregresif, E-score dan Least Square
Support Vector Machine. Institut Teknologi Sepuluh November.
Surabaya.
Kusumandari, D. E., Fakhrurroja, H., Hutagalung, S., Kumbara, B., Simarmata, J.,
Turnip, A. (2014). Removal of EOG Artifacts: Comparison of ICA
Algorithm from Recording EEG. International Conferences on
Technology, Informatics, Management, Engineering & Environtment.
Middendorf, Matthew., McMillan, G., Calhoun, G., Jones, Keith S. (2000). Braincomputer interfaces based on the steady-state visual-evoked response,
IEEE TRANSACTIONS ON REHABILITATION ENGINEERING ,
VOL.8.
47
Nugroho, A Satrio., Witarto, A Budi., Handoko, Dwi. (2003). Support Vector
Mahcine-Teori
dan
Aplikasinya
dalam
Bioinformatika.
IlmuKomputer.com.
Pardede, J., Turnip, M., Manalu, D. R., Turnip, A. (2015). Adaptive Recurrent
Neural Network for Reduction og Noise and Estimation of Source from
Recorded EEG Signals. ARPN Journal of Engineering and Applied
Sciences, vol.10 hlm.993-997.
Proakis, John G., Manolakis, Dimitris G. (2007). Digital Signal Processing Fourth
Edition. New Jersey:Pearson education.
Ranky, G. N., & Adamovich, S. (2010). Analysis of a Commercial EEG Device
for the Control of a Robot Arm. IEEE.
Sanei, S., Chambers, J.A. (2007). EEG Signal Processing. John Wiley and Sons.
Sellers, Eric W., Keusienski, Dean J., McFarland, Dennis J., Vaughan, Teresa M.,
Wolpaw, Jonathan R. (2006). A P300 event-related potential brain–
computer interface (BCI): The effects of matrix size and inter stimulus
interval on performance. Biological Psychology, vol.73 hlm.242-252.
Shishkin, S. L., Ganin, I. P., & Kaplan, A. Y. (2011 ). Event-related potentials in a
moving matrix modification of the P300 brain–computer interface
paradigm. Neuroscience Letters, vol.496 hlm.95-99.
Singla, Rajesh., B.a,Haseena. (2013). BCI Based Wheelchair Control Using
Steady State Visual Evoked Potentials adn Support Vector Machine.
International Journal of Soft Computing and Engineering (IJSCE),
vol.3.
Soetraprawata, D., Turnip, A. (2013). Autoregressive Integrated Adaptive Neural
Network
Classifier
for
EEG-P300
Classification.
Journal
of
Mechatronics, Electrical Power and Vehicular Technology, vol.4
hlm.1-8.
Sugiarto, Indar. (t.t). Sistem BCI Berbasis Low Density EEG. Fakultas Teknologi
Industri, Universitas Kristen Petra.
Swords, D., Sandygulova, A., Abdalla, S., & O'Hare, G. M. (2013).
Electroencephalograms for Ubiquitous Robotic Systems. Procedia
Computer Science, vol.21 hlm.174-182.
48
Turnip, A. (2012). Feature Extraction and Classification of EEG-P300 Signals,
and Its application to Online Brain Computer Interface. (Tesis). The
Graduate School, Pusan National University, Korea.
Trunip, A., Kusumandari, D. E. (2014). Improvement of BCI performance
throught nonlinear independent component analysis extraction. Journal
of Computer , vol.9 hlm.688-695.
Turnip, A., Hutagalung, S. S., Pardede, J., Soetraprawata, D. (2013). P300
Detection using multilayer neural networks based adaptive feature
extraction method. International Journal of Brain and Cognitive
Science, vol.2 hlm.63-75.
Turnip, A., Hong, K.-S. (2012). Classifying mental activities from EEG-P300
signals using adaptive neural network. International Journal of
Innovative Computing, Information and Control (IJICIC), vol.8.
Turnip, A., Hong, K.-S., Jeong, M.-Y. (2011). Real-time feature extraction of
P300 component using adaptive nonlinear principal component
analysis. Journal of BioMedical Engineering OnLine, vol.10, no.83.
Turnip, A., & Soetraprawata, D. (2013). Performance of EEG-P300 Classification
Using Backpropagation Neural Networks. Journal of Mechatronics,
Electrical Power and Vehicular Technology.
Turnip, A. (2015). Comparison of ICA-based JADE and SOBI Methods EOG
Artifacts Removal. Journal of Medical and Bioengineering.
Turnip, A., Siahaan, M. (2014). Adaptive Principal Component Analysis based
Recursive Least Square for Artifact Removal of EEG Signals.
Advanced Science Letter, vol.20 hlm.2034-2037.
Turnip, A., Setiawan, I. R., Junaidi, E., Hoa, N. L. (2014). An Experiment of
Ocular Artifacts Elimination from EEG Signals Using ICA and PCA
Methods. Journal of Mechatronics, Electrical Power and Vehicular
Technology, vol.5 hlm.129-138.
Turnip, A. (2015). Noise Reduction and Brain Mapping based Robust Principal
Component Analysis. 12th IEEE International Conference on
Networking, Sensing, and Control.
49
Turnip, A. (2014). Automatic Artifacts Removal of EEG Signals using Robust
Principal
Component
Analysis.
International
Conferences
on
Technology, Informatics, Management, Engineering & Environtment.
Turnip, A., Soetraprawata, D., Hariyadi., Kusumandari, D. E. (2013). Sequential
extended Kalman Filter on EEG extraction and classification. Teknologi
Indonesia, vol.36.
Turnip, A., Kusumandari, D. E. (2014). Nonlinear Adaptive Filter for EEG-P300
Extraction based Four Inter-Stimulus Intervals. Journal of Information
and Communication Technology.
Wang, Y.-K., Chen, S.-A., Lin, C.-T. (2014). An EEG-based brain– computer
interface for dual task driving detection. Neurocomputing, vol.129
hlm.85-93.
Wikipedia.
(2013).
Kebohongan
[Online]
Tersedia
di
http://id.wikipedia.org/wiki/Kebohongan [Diakses 12 Desember 2014]
Zandi, A Shahidi., Javidan, Manouchehr., Dumont, Guy A., Tafreshi, Reza.
(2010). Automated Real-Time Epileptic Seizure Detection in Scalp
EEG Recordings Using an Algorithm Based on Wavelet Packet
Transform.
IEEE
ENGINEERING, vol.57.
TRANSACTIONS
ON
BIOMEDICAL
DAFTAR PUSTAKA
Aapo, Hyvarinen., Juha, Karhunen., Erkki, Oja. (2001). Independent Component
Analysis, John Wiley & Sons, inc: New York.
Abootalebi, Vahid., Moradi, M Hassan., Khalilzadeh, M Ali. (2009). A new
approach for EEG feature extraction in P300-based lie detection. Computer
methods and programs in biomedicine, vol.94 hlm.48-57.
Ahmed, K. S. (2011). Wheelchair movement control via human eye blinks.
American journal of biomedical engineering, vol.1 hlm.55-58.
Akhtar, M.T., Mitsuhashi, W., James, C.J. Employing spatially constrained ICA
and wavelet denoising, for automatic removal of artifacts from multichannel
EEG data. (2012). Signal Processing, vol.27 pp.401–416.
Akram, F., Han, H.-S., & Kim, T.-S. (2014). A P300-based brain computer
interface system for words typing. Computers in Biology and Medicine,
vol.45 hlm.118-125.
Amri, Moch. Choirul., Adil, Ratna. (t.t). Rancang Bangun Modul EEG Untuk
Pengkalisifikasikan Keaadaan Stress dengan FIR. Departemen Teknik
Elektronika, Politeknik Elektronika Negeri Surabaya.
Bi, Luzheng., Fan,Xin-An., Liu,Yili. (2013). EEG-Based Brain-Controlled Mobile
Robots: A Survey. IEEE Transactions ON Human-Machine Systems,
Vol. 43, No. 2, March 2013.
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B.,
Kubler, A., Perelmouter, J., Taub, E., Flor, H. (1999). "A Spelling
Device for the Paralysed". Nature, vol.398 hlm. 297-298.
Gourab, Krishnaj., Schmit, Brian D. (2010). Changes in movement-related b-band
EEG signals in human spinal cord injury. Clinical Neurophysiology,
vol.121 hlm.2017–2023.
Gunadi, Aris., Gede, I., Harjoko, Agus. (2012). Telaah Metode-Metode
Pendeteksi Kebohongan. IJCCS, vol.6 hlm.35-46.
Bagus Kumbara,2015
KLASIFIKASI DAN EKSTRAKSI SINYAL EEG-P300 MENGGUNAKAN SUPPORT VECTOR MACHINE
UNTUK DETEKSI KEBOHONGAN
Universitas Pendidikan Indonesia | repository.upi.edu | perpustakaan.upi.edu
45
Halchenko, Yaroslav O., Hanson, Stephen J., Pearlmutter, Barak A. (2005).
Multimodal Integration: fMRI, MRI, EEG, MEG. hlm223-265.
Bagus Kumbara,2015
KLASIFIKASI DAN EKSTRAKSI SINYAL EEG-P300 MENGGUNAKAN SUPPORT VECTOR MACHINE
UNTUK DETEKSI KEBOHONGAN
Universitas Pendidikan Indonesia | repository.upi.edu | perpustakaan.upi.edu
46
Hazrati, M. K., & Erfanian, A. (2010). An online EEG-based brain–computer
interface for controlling hand grasp using an adaptive probabilistic
neural network. Medical Engineering & Physics, vol.32 hlm.730-739.
Hsu, Wei-Yen. (2010). EEG-based motor imagery classification using neurofuzzy prediction and wavelet fractal features. Journal of Neuroscience
Methods, vol.189 hlm.295-302.
Hwang, Han-Jeong., lim, Jeong-Hwan., Jung, Young-jin., Choi, Han., Lee, Sang
Woo., Im, Chang-Hwan. (2012). Development of an SSVEP-based BCI
spelling system adopting a QWERTY-style LED keyboard. Journal of
Neuroscience Methods, vol.208 hlm.59–65.
Jahankhani, Pari., Kodogiannis, Vassilis., Revett, Kenneth. (2006). EEG signal
classification using wavelet feature extraction and neural networks.
IEEE John Vincent Atanasoff 2006 International Symposium on
Modern Computing.
Kalcher, J., Flotzinger, D., Neuper, Ch., Golly, S., Pfurtscheller, G. (1996). Graz
braincomputer interface II: towards communication between humans
and computers based on online classification of three different EEG
patterns. Medical and Biological Engineering and Computing, vol.34
hlm.382-388.
Karyawan, Moch A., Arifin, Agus Z., Saikhu, Ahmad. (2011). Klasisfikasi Sinyal
EEG Menggunakan Koefisien Autoregresif, E-score dan Least Square
Support Vector Machine. Institut Teknologi Sepuluh November.
Surabaya.
Kusumandari, D. E., Fakhrurroja, H., Hutagalung, S., Kumbara, B., Simarmata, J.,
Turnip, A. (2014). Removal of EOG Artifacts: Comparison of ICA
Algorithm from Recording EEG. International Conferences on
Technology, Informatics, Management, Engineering & Environtment.
Middendorf, Matthew., McMillan, G., Calhoun, G., Jones, Keith S. (2000). Braincomputer interfaces based on the steady-state visual-evoked response,
IEEE TRANSACTIONS ON REHABILITATION ENGINEERING ,
VOL.8.
47
Nugroho, A Satrio., Witarto, A Budi., Handoko, Dwi. (2003). Support Vector
Mahcine-Teori
dan
Aplikasinya
dalam
Bioinformatika.
IlmuKomputer.com.
Pardede, J., Turnip, M., Manalu, D. R., Turnip, A. (2015). Adaptive Recurrent
Neural Network for Reduction og Noise and Estimation of Source from
Recorded EEG Signals. ARPN Journal of Engineering and Applied
Sciences, vol.10 hlm.993-997.
Proakis, John G., Manolakis, Dimitris G. (2007). Digital Signal Processing Fourth
Edition. New Jersey:Pearson education.
Ranky, G. N., & Adamovich, S. (2010). Analysis of a Commercial EEG Device
for the Control of a Robot Arm. IEEE.
Sanei, S., Chambers, J.A. (2007). EEG Signal Processing. John Wiley and Sons.
Sellers, Eric W., Keusienski, Dean J., McFarland, Dennis J., Vaughan, Teresa M.,
Wolpaw, Jonathan R. (2006). A P300 event-related potential brain–
computer interface (BCI): The effects of matrix size and inter stimulus
interval on performance. Biological Psychology, vol.73 hlm.242-252.
Shishkin, S. L., Ganin, I. P., & Kaplan, A. Y. (2011 ). Event-related potentials in a
moving matrix modification of the P300 brain–computer interface
paradigm. Neuroscience Letters, vol.496 hlm.95-99.
Singla, Rajesh., B.a,Haseena. (2013). BCI Based Wheelchair Control Using
Steady State Visual Evoked Potentials adn Support Vector Machine.
International Journal of Soft Computing and Engineering (IJSCE),
vol.3.
Soetraprawata, D., Turnip, A. (2013). Autoregressive Integrated Adaptive Neural
Network
Classifier
for
EEG-P300
Classification.
Journal
of
Mechatronics, Electrical Power and Vehicular Technology, vol.4
hlm.1-8.
Sugiarto, Indar. (t.t). Sistem BCI Berbasis Low Density EEG. Fakultas Teknologi
Industri, Universitas Kristen Petra.
Swords, D., Sandygulova, A., Abdalla, S., & O'Hare, G. M. (2013).
Electroencephalograms for Ubiquitous Robotic Systems. Procedia
Computer Science, vol.21 hlm.174-182.
48
Turnip, A. (2012). Feature Extraction and Classification of EEG-P300 Signals,
and Its application to Online Brain Computer Interface. (Tesis). The
Graduate School, Pusan National University, Korea.
Trunip, A., Kusumandari, D. E. (2014). Improvement of BCI performance
throught nonlinear independent component analysis extraction. Journal
of Computer , vol.9 hlm.688-695.
Turnip, A., Hutagalung, S. S., Pardede, J., Soetraprawata, D. (2013). P300
Detection using multilayer neural networks based adaptive feature
extraction method. International Journal of Brain and Cognitive
Science, vol.2 hlm.63-75.
Turnip, A., Hong, K.-S. (2012). Classifying mental activities from EEG-P300
signals using adaptive neural network. International Journal of
Innovative Computing, Information and Control (IJICIC), vol.8.
Turnip, A., Hong, K.-S., Jeong, M.-Y. (2011). Real-time feature extraction of
P300 component using adaptive nonlinear principal component
analysis. Journal of BioMedical Engineering OnLine, vol.10, no.83.
Turnip, A., & Soetraprawata, D. (2013). Performance of EEG-P300 Classification
Using Backpropagation Neural Networks. Journal of Mechatronics,
Electrical Power and Vehicular Technology.
Turnip, A. (2015). Comparison of ICA-based JADE and SOBI Methods EOG
Artifacts Removal. Journal of Medical and Bioengineering.
Turnip, A., Siahaan, M. (2014). Adaptive Principal Component Analysis based
Recursive Least Square for Artifact Removal of EEG Signals.
Advanced Science Letter, vol.20 hlm.2034-2037.
Turnip, A., Setiawan, I. R., Junaidi, E., Hoa, N. L. (2014). An Experiment of
Ocular Artifacts Elimination from EEG Signals Using ICA and PCA
Methods. Journal of Mechatronics, Electrical Power and Vehicular
Technology, vol.5 hlm.129-138.
Turnip, A. (2015). Noise Reduction and Brain Mapping based Robust Principal
Component Analysis. 12th IEEE International Conference on
Networking, Sensing, and Control.
49
Turnip, A. (2014). Automatic Artifacts Removal of EEG Signals using Robust
Principal
Component
Analysis.
International
Conferences
on
Technology, Informatics, Management, Engineering & Environtment.
Turnip, A., Soetraprawata, D., Hariyadi., Kusumandari, D. E. (2013). Sequential
extended Kalman Filter on EEG extraction and classification. Teknologi
Indonesia, vol.36.
Turnip, A., Kusumandari, D. E. (2014). Nonlinear Adaptive Filter for EEG-P300
Extraction based Four Inter-Stimulus Intervals. Journal of Information
and Communication Technology.
Wang, Y.-K., Chen, S.-A., Lin, C.-T. (2014). An EEG-based brain– computer
interface for dual task driving detection. Neurocomputing, vol.129
hlm.85-93.
Wikipedia.
(2013).
Kebohongan
[Online]
Tersedia
di
http://id.wikipedia.org/wiki/Kebohongan [Diakses 12 Desember 2014]
Zandi, A Shahidi., Javidan, Manouchehr., Dumont, Guy A., Tafreshi, Reza.
(2010). Automated Real-Time Epileptic Seizure Detection in Scalp
EEG Recordings Using an Algorithm Based on Wavelet Packet
Transform.
IEEE
ENGINEERING, vol.57.
TRANSACTIONS
ON
BIOMEDICAL