Abstract
In this paper, four class motor imagery classification has been studied for brain computer interface. Feature investigations were conducted on the Enobio device, firstly with all 8 channels (F3, F4, T7, C3, C4, Cz, T8 and Pz) and subsequently with 3 selected channels (C4 left hand, C3 right hand, C3 and C4 both hand and Cz both feet) in alpha and beta rhythm in order to establish the active networks. Five volunteers were participated, the volunteers were instructed to perform motor imagery tasks, such as to imagine the opening and closing of the left and right hand, both hands, and both feet movement. Electroencephalogram (EEG) data were collected and offline signals processing were performed. Discrete wavelet transform (DWT) was used for feature extraction, while difference classifications methods such as multilayer perceptron (MLP), RBFNetwork, and K-Nearest Neighbors (KNN) were implemented. Best classification of MLP over KNN and RBFNetwork was noticed, whereas the highest accuracy was achieved at sym8 wavelet using DWT based feature extraction. On average over the subjects the selected channel accuracies were in the range of 86.61 %. Whereas for all the channels, accuracies were in range of 78.37 %. The study has shown that the classification accuracy can significantly improve by using specific channels for the EEG classification rather than using all EEG channels a time.
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References
Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain–computer interfaces for communication and control. Clin Neurophysiol 113:767–791
Pfurtscheller G, Brunner C, Schlogl A, Lopes da Silva FH (2006) Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 31:153–159
Neuper C, Scherer R, Wriessnegger S, Pfurtscheller G (2009) Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface. Clin Neurophysiol 120:239–247
Pfurtscheller FHLSG (1999) Event-related EEG/MEG synchronization and desynchronization: basic principle. Clin Neurophysiol 110:1842–1857
Pfurtscheller CNG, Flotzinger D, Pregenzer M (1997) EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr Clin Neurophysiol 103:642–651
Neuper C, Pfurtscheller G (2001) Evidence for distinct beta resonance frequencies in human EEG related to specific sensorimotor cortical areas. Clin Neurophysiol 112:2084–2097
Pfurtscheller G, Neuper C, Pichler-Zalaudek K, Edlinger G, da Silva FHL (2000) Do brain oscillations of different frequencies indicate interaction between cortical areas in humans? Neurosci Lett 286:66–68
Neuper C, Pfurtscheller G (2001) Event-related dynamics of cortical rhythms: frequency-specific features and functional correlates. Int J Psychophysiol 43:41–58
Yacine B, Amal F, Walter B (2014) Significant improvement in one-dimensional cursor control using Laplacian electroencephalography over electroencephalography. J Neural Eng 11:035014
Adeli H, Zhou Z, Dadmehr N (2003) Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 123:69–87
Alomari MH, Awada EA, Samaha A, Alkamha K (2014) Wavelet-based feature extraction for the analysis of EEG signals associated with imagined fists and feet movements. Comput Inf Sci 7 (2014)
Mohamed E (2014) Enhancing EEG signals in brain computer interface using wavelet transform. Int J Inf Electron Eng 4 (2014)
Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain–computer interfaces. J Neural Eng 4:R1
Bishop CM (1996) Neural networks for pattern recognition. Oxford University Press, Oxford
Duda PEHRO, Stork DG (2001) Pattern recognition, 2nd edn. Wiley-Interscience
Hari NFR, Avikainen S, Kirveskari E, Salenius S, Rizzolatti G (1998) Activation of human primary motor cortex during action observation: a neuromagnetic study. Neurobiology 95:15061–15065
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The author would like to thank Universiti Teknologi PETRONAS for funding this research project under Graduate Assistantship Scheme.
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Abdalsalam, E., Yusoff, M.Z., Kamel, N., Malik, A.S., Mahmoud, D. (2017). Classification of Four Class Motor Imagery for Brain Computer Interface. In: Ibrahim, H., Iqbal, S., Teoh, S., Mustaffa, M. (eds) 9th International Conference on Robotic, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 398. Springer, Singapore. https://doi.org/10.1007/978-981-10-1721-6_32
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DOI: https://doi.org/10.1007/978-981-10-1721-6_32
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