@ persidangan{Sudirman2018136,
tajuk = {Penunjuk profil untuk kanak-kanak autistik menggunakan potensi biosignal EEG untuk tugas deria},
pengarang = {R Sudirman dan SS Hussin dan A G Airij dan C Z Hai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058032461&doi = 10.1109% 2fICBAPS.2018.8527403&rakan kongsi = 40&md5=30dbb1596f4a0529332713c087bd788d},
doi = {10.1109/ICBAPS.2018.8527403},
isbn = {9781538612774},
tahun = {2018},
tarikh = {2018-01-01},
jurnal = {2nd Persidangan Antarabangsa mengenai Analisis BioSignal, Pemprosesan dan Sistem, ICBAPS 2018},
halaman = {136-141},
penerbit = {Institut Jurutera Elektrik dan Elektronik Inc.},
abstrak = {Elektroensefalografi (LIHAT) ialah ukuran voltan yang disebabkan oleh aktiviti saraf di dalam otak. EEG ialah alat yang disyorkan untuk mendiagnosis masalah neurologi kerana ia tidak invasif dan boleh direkodkan dalam tempoh masa yang lebih lama.. Kanak-kanak Autism Spectrum Disorder (ASD) mengalami kesukaran untuk meluahkan emosi mereka kerana ketidakupayaan mereka memproses maklumat yang betul dalam otak. Oleh itu, penyelidikan ini bertujuan untuk membina profil deria dengan bantuan potensi biosignal EEG untuk membezakan antara tindak balas deria yang berbeza. Isyarat EEG yang diperoleh dalam penyelidikan ini mengenal pasti keadaan emosi yang berbeza seperti berfikiran positif atau super-pembelajaran dan relaksasi ringan dan berada dalam julat frekuensi 8-12 Hertz. 64 kanak-kanak mengambil bahagian dalam penyelidikan ini antaranya 34 adalah kanak-kanak dengan ASD dan 30 adalah kanak-kanak biasa. Data EEG dikod semula manakala semua kanak-kanak dibekalkan dengan vestibular, visual, bunyi, rasa dan rangsangan deria vestibular. Data EEG mentah telah ditapis dengan bantuan analisis komponen bebas (ICA) menggunakan transformasi wavelet dan perisian EEGLAB. Nanti, untuk membina profil deria, penghampiran entropi, min dan sisihan piawai telah diekstrak daripada isyarat EEG yang ditapis. Bersama-sama dengan itu, data EEG yang ditapis juga disalurkan ke rangkaian saraf (NN) algoritma yang telah dilaksanakan dalam MATLAB. Keputusan daripada isyarat EEG yang diperoleh menggambarkan bahawa semasa fasa rangsangan deria, respons semua kanak-kanak autisme berada dalam keadaan tidak stabil. Penemuan ini akan melengkapkan dan membantu strategi pembelajaran mereka pada masa hadapan. © 2018 IEEE.},
nota = {dipetik oleh 0},
kata kunci = {Kanak-kanak Autistik, Pemprosesan Isyarat Bioperubatan, Otak, Kanak-kanak dengan Autisme, Elektroensefalografi, Elektrofisiologi, Anggaran Entropi, Analisis Komponen Bebas, MATLAB, Rangkaian Neural, Masalah Neurologi, Analisis Deria, Profil Deria, Rangsangan Deria, Paket Wavelet Berubah},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}

@ persidangan{Sudirman2014626,
tajuk = {Sensory responses of autism via electroencephalography for Sensory Profile},
pengarang = {R Sudirman and S S Hussin},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946435600&doi=10.1109%2fICCSCE.2014.7072794&rakan kongsi = 40&md5=3e6f1cfe19eae4fad359d2493aebd7e0},
doi = {10.1109/ICCSCE.2014.7072794},
isbn = {9781479956869},
tahun = {2014},
tarikh = {2014-01-01},
jurnal = {Prosiding - 4th IEEE International Conference on Control System, Pengkomputeran dan Kejuruteraan, ICCSCE 2014},
halaman = {626-631},
penerbit = {Institut Jurutera Elektrik dan Elektronik Inc.},
abstrak = {The aim of this study is to investigate the brain signals of autism children through electroencephalography (LIHAT) associated to physical tasks. The physical task was meant to stimulate the sensitivity correlation of sensory response of a child. A group of autism children was chosen for this study and were given by five sensory stimulations which are audio, rasa, sentuhan, visual and vestibular. The acquisition of brain signals was acquainted using EEG Neurofax 9200 and the electrode positions were using 10-20 International System placements. The preprocessing signals were analyzed using independent component analysis (ICA) using EEGLAB Software and Discrete Wavelet Transform (DWT). The alpha wave was selected by level 6 decomposition and the extracted features represents the characteristic of the sensory task. The means, standard deviations and approximation entropy were extracted on the clean signals and forms into Sensory Profile (Sensory Profiling). From the overall results, the behavior of each autism children has been observed unstable emotion while running the sensory stimulation. The observation also helps to improve their learning strategy for the future work in assessment. © 2014 IEEE.},
nota = {dipetik oleh 3},
kata kunci = {Autisme, Discrete Wavelet Transforms, Penyakit, Elektroensefalografi, Elektrofisiologi, Analisis Komponen Bebas, International System, Belajar, Analisis Deria, Profil Deria, Sensory Profiling, Rangsangan Deria, Pemprosesan isyarat, Standard Deviation, Paket Wavelet Berubah},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}

@ persidangan{Sudirman2010401,
tajuk = {Study of electroencephalography signal of autism and down syndrome children using FFT},
pengarang = {Sudirman and S Saidin and N Mat Safri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79251542066&doi=10.1109%2fISIEA.2010.5679434&rakan kongsi = 40&md5=17fce4f69b27a3cc644f36c118b6ec6e},
doi = {10.1109/ISIEA.2010.5679434},
isbn = {9781424476473},
tahun = {2010},
tarikh = {2010-01-01},
jurnal = {ISIEA 2010 - 2010 IEEE Symposium on Industrial Electronics and Applications},
halaman = {401-406},
abstrak = {Elektroensefalografi (LIHAT) signal between normal and special children is slightly different. Different types of special children will generate different shape of EEG patterns depend on their neurological function. This paper demonstrates the classification of EEG signal for special children: to determine and to classify level and pattern of EEG signal for autism and Down syndrome children. EEG signal was recorded and captured from normal and special children based on their visual response using Visual Evoked Potential (VEP) method. The data is analyzed using Fast Fourier Transform (FFT), so that, normal and special children can be distinguished based on alpha (α) value. Akibatnya, alpha value for normal children at 10 Hz is higher than autism and Down syndrome children. A friendly user interface was built for easy storage and visualization. ©2010 IEEE.},
nota = {dipetik oleh 15},
kata kunci = {Alpha Value, Autisme, Sindrom Down, EEG Signals, Elektroensefalografi, Elektrofisiologi, Fast Fourier Transforms, Elektronik Perindustrian, Metadata, Antara Muka Pengguna, Visual Evoked Potential, Visualization},
pubstate = {diterbitkan},
tppubtype = {persidangan}
}