Pengaruh Ciri Temporal, Spasial, dan Frekuensi pada Klasifikasi Motor Imagery
DOI:
https://doi.org/10.25126/jtiik.2022935715Abstrak
Interaksi mesin-komputer merupakan suatu keniscayaan dan akan menjadi bagian yang tidak terpisahkan dari kehidupan dalam waktu dekat, terutama di bidang rekayasa rehabilitasi. Salah satu bidang yang berkembang adalah klasifikasi Motor Imagery (MI) berbasis sinyal EEG. Set data pada studi ini diambil dari BCI Competition IV - 2b. Prapemrosesan data dilakukan dengan menggunakan BPF Butterworth orde 5 dengan frekuensi cut-off sebesar 8 – 30 Hz. Pada studi ini, dilakukan investigasi pengaruh ciri temporal; spasial; dan frekuensi serta kombinasi ciri temporal-spasial dan temporal-spasial-frekuensi. Ciri temporal diekstraksi dengan menggunakan ICA, ciri spasial dengan CSP, dan frekuensi dengan STFT. Terdapat empat pengklasifikasi yang digunakan, yaitu SVM; RF; k-NN; dan NB. Salah satu temuan pada studi ini adalah meskipun digunakan kombinasi ciri temporal-spasial maupun temporal-spasial-frekuensi, nilai akurasi yang diperoleh sama, yaitu sekitar 72%. Kinerja kedua kombinasi ciri ini masih kalah apabila dibandingkan dengan hanya menggunakan ciri independen temporal dengan nilai akurasi mencapai 73%. Selain itu, pengklasifikasi RF memberikan kinerja yang paling baik dibandingkan dengan SVM; k-NN; serta NB.
Abstract
Human-computer interaction is a necessity and will be deployed in the near future, especially in rehabilitation engineering. One of the development is focused on the classification of Imagery Motor (MI) based on EEG signals. In this study, the dataset is taken from BCI Competition IV - 2b. The first step of the classification process is data preprocessing that is performed using BPF Butterworth 5th order with a cut-off frequency of 8 - 30 Hz. The aim of this study is to investigate the effect of independent feature such as temporal, spatial, frequency, and the combination of temporal-spatial and temporal-spatial-frequency features. Temporal feature is extracted using ICA, spatial feature using CSP, and frequency feature using STFT. In this study, four classifiers are used, i.e., SVM; RF; k-NN; and NB. One of the main findings in this study is that although the combination of temporal-spatial and temporal-spatial-frequency features is used, the accuracy value of 72% are obtained. The performance of these two combinations of features is still inferior when compared to independent temporal feature with an accuracy value of 73%. In addition, RF classifier provides the best performance compared to SVM; k-NN; and NB.
Keywords: motor imagery, temporal, spatial, frequency, random forest
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