Hasil Pencarian  ::  Simpan CSV :: Kembali

Abstrak :
ABSTRAK Dengan tujuan menghasilkan sebuah sistem klasifikasi gerakan genggaman tangan kanan berbasis alat EEG EMOTIV Epoc+ yang optimal dan mengacu pada elemen-elemen tahapan Brain-Computer Interface (BCI), telah didapatkan kombinasi elemen-elemen tahapan BCI dengan nilai akurasi klasifikasi paling tinggi. Adapun kombinasi elemen-elemen tersebut adalah sebagai berikut: penggunaan Independent Component Analysis (ICA), analisis spektrum oleh Fast Fourier Transform (FFT), fitur power maksimum mu berikut frekuensinya dan fitur power maksimum beta berikut frekuensinya, dan classifier Probabilistic Neural Network (PNN). Nilai akurasi klasifikasi yang didapat yaitu 81,2% untuk training dan 69,5% untuk testing. Perbandingan nilai akurasi dari perpaduan kombinasi, kondisi eksperimen, dan data EEG eksternal disediakan untuk keperluan analisis nilai akurasi klasifikasi.

---

ABSTRACT Has been obtained combination of elements of BCI stages providing the highest value of classification accuracy with the aim of producing an optimum classification system based on EEG device EMOTIV Epoc+ for right-hand grasp movement, by referring to Brain Computer Interface (BCI) stage element. The combinations of elements are the use of Independent Component Analysis (ICA), spectrum analysis by Fast Fourier Transform (FFT), maximum mu power with its frequency and maximum beta power with its frequency as features, and classifier Probabilistic Neural Network (PNN). The highest values of classification accuracy are 81,2% for training and 69,5% for testing. The comparison of accuracy value from the combination unification, experiment condition, and external EEG data are provided for the purpose of value analysis of classification accuracy.

Abstrak :
Keterbatasan dalam pengoperasian kursi roda membuat ketidaknyamanan yang besar bagi penggunanya. Salah satu metode yang dapat membantu kaum yang mengalami keterbatasan dalam mengoperasikan kursi roda adalah suatu sistem yang disebut Brain-Computer Interface. Sistem ini menggunakan elektroensefalografi (EEG) sebagai sarana komunikasi antara sinyal otak pengguna dan mekanisme pengendalian kursi roda. Proses akuisisi data melibatkan penggunaan elektroda AgCl 8 kanal, Raspberry Pi 4 Model B, dan ADS1299. Teknik pengolahan sinyal, termasuk bandpass filter, Independent Component Analysis (ICA), dan analisis Power Spectral Density (PSD), diimplementasikan untuk meningkatkan kualitas sinyal EEG yang diperoleh. Tahap klasifikasi menggunakan Support Vector Machine (SVM) untuk menginterpretasikan sinyal yang telah diproses, mencapai akurasi yang mengesankan sebesar 90%, presisi sebesar 91,4%, dan sensitivitas sebesar 90%. ......Limitations in wheelchair operation create great inconvenience for users. One method that can help people who experience limitations in operating a wheelchair is a system called Brain-Computer Interface. This system uses electroencephalography (EEG) as a means of communication between the user's brain signals and the wheelchair control mechanism. The data acquisition process involves the use of 8-channel AgCl electrodes, a Raspberry Pi 4 Model B, and an ADS1299. Signal processing techniques, including bandpass filter, Independent Component Analysis (ICA), and Power Spectral Density (PSD) analysis, were implemented to improve the quality of the acquired EEG signals. The classification stage used Support Vector Machine (SVM) to interpret the processed signals, achieving an impressive accuracy of 90%, precision of 91.4%, and sensitivity of 90%.

Abstrak :
Brain-Computer Interface (BCI) merupakan sebuah sistem yang mampu menerjemahkan sinyal-sinyal otak menjadi perintah kepada berbagai devais keluaran. Teknologi ini kini sedang berkembang pesat terutama untuk keperluan rehabilitasi gerak bagi orang-orang yang telah kehilangan kemampuan geraknya. Dalam penelitian ini, dirancang sebuah sistem BCI yang mampu menerjemahkan sinyal otak seseorang ketika sedang melakukan pembayangan gerak (motor imagery) untuk gerakan tangan menggenggam dan membuka. Hasil terjemahan tersebut dapat digunakan untuk menggerakkan sebuah antarmuka yang membantu orang tersebut untuk bergerak menggenggam dan membuka tangan secara real-time. Sistem BCI ini menggunakan perangkat akuisisi data yang terdiri dari Raspberry Pi 4 dan ADS1299 Analog-to-Digital Converter. Sistem ini juga dikembangkan dengan menggunakan berbagai algoritma pemrosesan dan klasifikasi data, mulai dari Independent Component Analysis, Support Vector Machine, Linear Discriminant Analysis, k-Nearest Neighbours, dan Random Forest. Akurasi hasil testing klasifikasi yang dilakukan oleh sistem ini bernilai 64,6% untuk mengklasifikasi 3 jenis pembayangan gerak (menggenggam, membuka, dan diam) menggunakan algoritma SVM serta 94,7% untuk klasifikasi 2 jenis pembayangan gerak (menggenggam dan membuka) menggunakan algoritma Random Forest. ......Brain-Computer Interface (BCI) is a system which can translate brain signals to command various output devices. This technology had been developing rapidly, especially for movement rehabilitation purposes for people with motoric disabilities. In this research, a BCI system has been developed which can translate one’s brain signals when one is imagining doing hand movement (motor imagery). The translation result can be used to drive an interface in real-time. This BCI system utilize an acquisition device, consisting of Raspberry Pi 4 and ADS1299 Analog-to-Digital Converter. Besides, this system has also been developed using several algorithms for processing and classifying data, namely Independent Component Analysis, Support Vector Machine, Linear Discriminant Analysis, k-Nearest Neighbours, and Random Forest. Testing accuracy for this system yielded a 64.6% for classifying three types of motor imagery (hand grasping, hand opening, and resting) with SVM, and 94.7% for classifying two types of motor imagery (hand grasping and hand opening only) using Random Forest.

Abstrak :
The topics treated in this handbook cover all areas of games and entertainment technologies, such as digital entertainment; technology, design/art, and sociology. The handbook consists of contributions from top class scholars and researchers from the interdisciplinary topic areas. The aim of this handbook is to serving as a key reference work in the field and provides readers with a holistic picture of this interdisciplinary field covering technical issues, aesthetic/design issues, and sociological issues. At present, there is no reference work in the field that provides such a broad and complete picture of the field. Engineers and researchers who want to learn about this emerging area will be able to find adequate answers regarding technology issues on digital entertainment. Designers and artists can learn how their skills and expertise can contribute to this emerging area. Also researchers working in the field of sociology and psychology will find how their experience and knowledge are connected to other areas such as technology and art/design. Although topics are written by foremost experts from the field, the description for each topic has been intended to be easily understandable but yet comprehensive enough so that it caters not only for the experts but also beginners and students in the field.