Klasifikasi Sinyal EEG pada Sistem BCI Pergerakan Jari Manusia Menggunakan Convolutional Neural Network

Authors

  • Rahmat Widadi Fakultas Teknik Telekomunikasi dan Elektro, Institut Teknologi Telkom Purwokerto https://orcid.org/0000-0003-1903-5556
  • Bongga Arif Widodo Fakultas Teknik Telekomunikasi dan Elektro, Institut Teknologi Telkom Purwokerto
  • Dodi Zulherman Fakultas Teknik Telekomunikasi dan Elektro, Institut Teknologi Telkom Purwokerto https://orcid.org/0000-0003-2105-0632

DOI:

https://doi.org/10.33633/tc.v19i4.4119

Keywords:

klasifikasi, EEG, motor imagery, lima jari manusia, 1D-CNN

Abstract

Pemanfaatan sistem Brain-Computer Interface (BCI) sebagai penghubung pikiran manusia dengan peralatan eksternal sangat bergantung pada keakuratan pengklasifikasian dan pengidentifikasian sinyal EEG khususnya gerak motor imagery. Kesuksesan deep learning, sebagai contoh Convolutional Neural Network (CNN), dalam proses klasifikasi pada berbagai bidang berpeluang untuk diimplementasikan pada klasifikasi gerak motor imagery. Pengimplementasian CNN untuk klasifikasi sinyal EEG motor imagery (MI-EEG) gerakan jari tangan diperkenalkan dalam tulisan ini. Rancangan sistem klasifikasi terdiri dari dua bagian yaitu convolution layer dan multilayer perceptron yang diimplementasikan menggunakan Python 3.7 dengan library TensorFlow 2.0 (Keras). Pengujian rancangan sistem dilakukan terhadap lima subjek dari data MI-EEG 5F dengan frekuensi pencuplikan 200 Hz. Pengujian melibatkan Kfold-cross validation dan analisis pada confusion matrix. Berdasarkan hasil pengujian, peningkatan ukuran kernel menghasilkan peningkatan rata-rata akurasi sistem. Sistem dengan akurasi terbaik diperoleh pada rancangan dengan jumlah kernel 50 sebesar 51,711%. Rancangan sistem menghasilkan kinerja yang melebihi hasil penelitian yang menjadi rujukan utama.

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Published

2020-11-26

Issue

Vol. 19 No. 4 (2020): November 2020

Section

Articles

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Copyright (c) 2020 Rahmat Widadi

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