Pengaruh Ciri Temporal, Spasial, dan Frekuensi pada Klasifikasi Motor Imagery

Penulis

Afin Muhammad Nurtsani, Muhammad Adib Syamlan, Agung Wahyu Setiawan

Abstrak

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

Teks Lengkap:

PDF

Referensi


AOH, Y., HSIAO, H.J., LU, M.K., MACEROLLO, A., HUANG, H.C., HAMADA, M., TSAI, C.H. & CHEN, J.C., 2019. Event-related desynchronization/synchronization in spinocerebellar ataxia type 3. Frontiers in Neurology, [online] 10(JUL), p.822. https://doi.org/10.3389/fneur.2019.00822

BLANKERTZ, B., MULLER, K.-R., CURIO, G., VAUGHAN, T. M., SCHALK, G., WOLPAW, J. R., SCHLOGL, A., NEUPER, C., PFURTSCHELLER, G., HINTERBERGER, T., SCHRODER, M., & BIRBAUMER, N., 2004. The BCI competition 2003: Progress and perspectives in detection and discrimination of EEG single trials. IEEE Transactions on Biomedical Engineering, 51(6), 1044–1051.

https://doi.org/10.1109/TBME.2004.826692

HA, K.-W., & JEONG, J.-W., 2019. Motor Imagery EEG Classification Using Capsule Networks. Sensors, 19(13), 2854. https://doi.org/10.3390/s19132854

HUANG, H.-H., CONDOR, A. & HUANG, H.J., 2020. Classification of EEG Motion Artifact Signals Using Spatial ICA. [online] Springer, Cham.pp.23–35. https://doi.org/10.1007/978-3-030-33416-1_2

JIN, J., MIAO, Y., DALY, I., ZUO, C., HU, D. & CICHOCKI, A., 2019. Correlation-based channel selection and regularized feature optimization for MI-based BCI. Neural Networks, 118, pp.262–270. https://doi.org/10.1016/j.neunet.2019.07.008

KORHAN, N., DOKUR, Z., & OLMEZ, T., 2019. Motor Imagery Based EEG Classification by Using Common Spatial Patterns and Convolutional Neural Networks. 2019 Scientific Meeting on Electrical-Electronics Biomedical Engineering and Computer Science (EBBT), 1–4. https://doi.org/10.1109/EBBT.2019.8741832

LIN, L., MENG, Y., CHEN, J. & LI, Z., 2015. Multichannel EEG compression based on ICA and SPIHT. Biomedical Signal Processing and Control, 20, pp.45–51. https://doi.org/10.1016/j.bspc.2015.04.001

LU, N., YIN, T., & JING, X., 2020. Deep Learning Solutions for Motor Imagery Classification: A Comparison Study. 2020 8th International Winter Conference on Brain-Computer Interface (BCI), 1–6. https://doi.org/10.1109/BCI48061.2020.9061612

MACHINGAL, P., THOUSIF, M., DORA, S., & SUNDARAM, S., 2020. Self-regulated Learning Algorithm for Distributed Coding Based Spiking Neural Classifier. 2020 International Joint Conference on Neural Networks (IJCNN), 1–7.

https://doi.org/10.1109/IJCNN48605.2020.9207620

PADFIELD, N., ZABALZA, J., ZHAO, H., MASERO, V. & REN, J., 2019. EEG-based brain-computer interfaces using motor-imagery: Techniques and challenges. Sensors (Switzerland), [online] 19(6), p.1423. https://doi.org/10.3390/s19061423

RAMMY, S.A., ABRAR, M., ANWAR, S.J. & ZHANG, W., 2020. Recurrent Deep Learning for EEG-based Motor Imagination Recognition. In: 3rd International Conference on Advancements in Computational Sciences, ICACS 2020. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICACS47775.2020.9055952

RASHID, M., SULAIMAN, N., P. P. ABDUL MAJEED, A., MUSA, R.M., AHMAD, A.F., BARI, B.S. & KHATUN, S., 2020. Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review. Frontiers in Neurorobotics. https://doi.org/10.1109/ICACS47775.2020.9055952

SCHALK, G., MCFARLAND, D. J., HINTERBERGER, T., BIRBAUMER, N., & WOLPAW, J. R., 2004. BCI2000: A general-purpose brain-computer interface (BCI) system. IEEE Transactions on Biomedical Engineering, 51(6), 1034–1043. https://doi.org/10.1109/TBME.2004.827072

SELIM, S., TANTAWI, M.M., SHEDEED, H.A. & BADR, A., 2018. A CSPAM-BA-SVM Approach for Motor Imagery BCI System. IEEE Access, 6, pp.49192–49208.

https://doi.org/10.1109/ACCESS.2018.2868178

TANGERMANN, M., MÜLLER, K. R., AERTSEN, A., BIRBAUMER, N., BRAUN, C., BRUNNER, C., LEEB, R., MEHRING, C., MILLER, K. J., MÜLLER-PUTZ, G. R., NOLTE, G., PFURTSCHELLER, G., PREISSL, H., SCHALK, G., SCHLÖGL, A., VIDAURRE, C., WALDERT, S., & BLANKERTZ, B., 2012. Review of the BCI competition IV. Frontiers in Neuroscience, JULY, 55. https://doi.org/10.3389/fnins.2012.00055

WANG, T., DONG, E., DU, S., & JIA, C., 2019. A Shallow Convolutional Neural Network for Classifying MI-EEG. 2019 Chinese Automation Congress (CAC), 5837–5841. https://doi.org/10.1109/CAC48633.2019.8996981

XU, J., ZHENG, H., WANG, J., LI, D. & FANG, X., 2020. Recognition of eeg signal motor imagery intention based on deep multi-view feature learning. Sensors (Switzerland), [online] 20(12), pp.1–16. https://doi.org/10.3390/s20123496

YANG, B., FAN, C., GUAN, C., GU, X., & ZHENG, M., 2019. A Framework on Optimization Strategy for EEG Motor Imagery Recognition. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 774–777. https://doi.org/10.1109/EMBC.2019.8857672

ZHAO, H., ZHENG, Q., MA, K., LI, H., & ZHENG, Y., 2021. Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification. IEEE Transactions on Neural Networks and Learning Systems, 32(2), 535–545. https://doi.org/10.1109/TNNLS.2020.3010780

ZHU, Y., ZHANG, C., POIKONEN, H., TOIVIAINEN, P., HUOTILAINEN, M., MATHIAK, K., RISTANIEMI, T. & CONG, F., 2020. Exploring Frequency-Dependent Brain Networks from Ongoing EEG Using Spatial ICA During Music Listening. Brain Topography, [online] 33(3), pp.289–302. https://doi.org/10.1007/s10548-020-00758-5




DOI: http://dx.doi.org/10.25126/jtiik.2022935715