Klasifikasi Sinyal Otak Motor Imagery Menggunakan Extreme Learning Machine Dan Discrete Fourier Transform - Fransiskus Cahyadi Putra Pranoto; Agus Wahyu Widodo; Muhammad Arif Rahman (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
The brain is the most important body organ that humans have to act as a process for all movements and thoughts in the human body. The brain emits a signal when doing an activity and can be captured by an interface device called brain computer interfaces. To stimulate brain signal activity a stimulus is used, namely an imagery motor. Imagery motors are representations of motor movements imagined by the brain. In this study using 3 datasets namely datasets that have been collected by researchers with muse devices with subjects numbering 20 and having an age range of 19-23 years, the second and third datasets are BCI Competition IIIA and IIIB which are publicly available at bbci.de. The BCI Competition IIIA and IIIB datasets will be used to compare the quality of the datasets collected by the researchers. Signal processing uses the Butterworth Filter Infinite Impulse Response method with a frequency range of 8 to 30 Hz. In this study a study was conducted on the implementation of feature extraction methods with the help of the Discrete Fourier Transform method and the classification of brain signals using the Extreme Learning Machine method that uses imagery motor stimuli. The results obtained were 44% accuracy for 5 classes, 85% and 90% for 2 classes using Muse datasets, 66.67% and 75% 4 classes using BCI Competition IIIA datasets and 93.33% 2 classes using BCI Competition IIIB datasets.