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Convolutional neural networks for P300 signal detection applied to brain computer interface

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Języki publikacji
EN
Abstrakty
EN
A Brain‐Computer Interface (BCI) is an instrument capa‐ ble of commanding machine with brain signal. The mul‐ tiple types of signals allow designing many applications like the Oddball Paradigms with P300 signal. We propose an EEG classification system applied to BCI using the con‐ volutional neural network (ConvNet) for P300 problem. The system consists of three stages. The first stage is a Spatiotemporal convolutional layer which is a succession of temporal and spatial convolutions. The second stage contains 5 standard convolutional layers. Finally, a lo‐ gistic regression is applied to classify the input EEG sig‐ nal. The model includes Batch Normalization, Dropout, and Pooling. Also, It uses Exponential Linear Unit (ELU) function and L1‐L2 regularization to improve the lear‐ ning. For experiments, we use the database Dataset II of the BCI Competition III. As a result, we get an F1‐score of 53.26% which is higher than the BN3 model.
Twórcy
autor
  • Hassan II University Of Casablanca, LIM@II‑FSTM, B.P. 146, Mohammedia 20650, Morocco
  • Hassan II University Of Casablanca, LIM@II‑FSTM, B.P. 146, Mohammedia 20650, Morocco
  • Hassan II University Of Casablanca, LIM@II‑FSTM, B.P. 146, Mohammedia 20650, Morocco
Bibliografia
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  • [3] H. Cecotti and A. Graser, “Convolutional Neural Networks for P300 Detection with Application to Brain‑Computer Interfaces”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 3, 2011, 433–445,10.1109/TPAMI.2010.125.
  • [4] D.‑A. Clevert, T. Unterthiner, and S. Hochreiter, “Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)”,arXiv:1511.07289 [cs], 2016.
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  • [9] V. J. Lawhern, A. J. Solon, N. R. Waytowich, S. M. Gordon, C. P. Hung, and B. J. Lance, “EEG‑Net: a compact convolutional neural network for EEG‑based brain–computer interfaces”, Journal of Neural Engineering, vol. 15, no. 5, 2018, 10.1088/1741‑2552/aace8c.
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  • [11] M. Liu, W. Wu, Z. Gu, Z. Yu, F. Qi, and Y. Li, “Deep learning based on Batch Normalization for P300 signal detection”, Neurocomputing, vol. 275, 2018, 288–297, 10.1016/j.neucom.2017.08.039.
  • [12] F. Lotte, L. Bougrain, A. Cichocki, M. Clerc, M. Congedo, A. Rakotomamonjy, and F. Yger, “A review of classification algorithms for EEG‑based brain–computer interfaces: a 10 year update”, Journal of Neural Engineering, vol. 15, no. 3, 2018, 10.1088/1741‑2552/aab2f2.
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  • [14] D. Masters and C. Luschi, “Revisiting Small Batch Training for Deep Neural Networks”, arXiv:1804.07612 [cs], 2018.
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-57ff06be-8c5d-4d41-a764-b635f25733a8
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