Fenqi Rong
ABSTRACT
Brain-computer interfaces (BCI) enable direct communication with external equipment, using neural activity as the control signal. Electroencephalogram (EEG) signals are usually selected as the control signal. For EEG signals obtained from a given experimental paradigm, a superior algorithm for feature extraction and classification is very significant. As one of the representative algorithms of deep learning, the convolutional neural network (CNN) has been widely used in the field of BCI. In this work, we introduce the filter-bank convolutional network (FBCNet) and propose an improved method. It mainly improves the network performance by modifying the loss function. The single loss function in the network is improved to the multi-loss fusion functions. Various loss functions are added to the network, and the characteristics of different loss functions are used to train the network to improve the network classification performance. This method of multi-loss fusion functions is validated on a dataset of 11 healthy subjects and compared with the other three benchmark algorithms. The result shows that the improved FBCNet produces a four-classes accuracy of 78.5%, which is superior to other algorithms.
- Ahn M, Jun S C. Performance variation in motor imagery brain–computer interface: a brief review[J]. Journal of neuroscience methods, 2015, 243: 103-110.Google Scholar
- Lam Q C, Nguyen L A T, Nguyen H K. Build Control Command Set Based on EEG Signals via Clustering Algorithm and Multi-Layer Neural Network[J]. Journal of Communications, 2018, 13(7): 406-411.Google ScholarCross Ref
- Bucci P, Galderisi S. Physiologic basis of the EEG signal[J]. Standard Electroencephalography in Clinical Psychiatry, 2011: 7-12.Google Scholar
- Shih J J, Krusienski D J, Wolpaw J R. Brain-computer interfaces in medicine[C]//Mayo clinic proceedings. Elsevier, 2012, 87(3): 268-279.Google Scholar
- Gouy-Pailler C, Congedo M, Brunner C, Nonstationary brain source separation for multiclass motor imagery[J]. IEEE transactions on Biomedical Engineering, 2009, 57(2): 469-478.Google Scholar
- Sun G, Hu J, Wu G. A novel frequency band selection method for common spatial pattern in motor imagery based brain computer interface[C]//The 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010: 1-6.Google Scholar
- Thomas K P, Guan C, Lau C T, A new discriminative common spatial pattern method for motor imagery brain–computer interfaces[J]. IEEE Transactions on Biomedical Engineering, 2009, 56(11): 2730-2733.Google ScholarCross Ref
- Pfurtscheller G, Brunner C, Schlögl A, Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks[J]. NeuroImage, 2006, 31(1): 153-159.Google ScholarCross Ref
- Jeunet C, Jahanpour E, Lotte F. Why standard brain-computer interface (BCI) training protocols should be changed: an experimental study[J]. Journal of neural engineering, 2016, 13(3): 036024.Google ScholarCross Ref
- Lemm S, Blankertz B, Curio G, Spatio-spectral filters for improving the classification of single trial EEG[J]. IEEE transactions on biomedical engineering, 2005, 52(9): 1541-1548.Google ScholarCross Ref
- Ang K K, Chin Z Y, Zhang H, Filter bank common spatial pattern (FBCSP) in brain-computer interface[C]//2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence). IEEE, 2008: 2390-2397.Google Scholar
- LeCun Y, Bottou L, Bengio Y, Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.Google ScholarCross Ref
- Kwon O Y, Lee M H, Guan C, Subject-independent brain–computer interfaces based on deep convolutional neural networks[J]. IEEE transactions on neural networks and learning systems, 2019, 31(10): 3839-3852.Google Scholar
- Sakhavi S, Guan C, Yan S. Learning temporal information for brain-computer interface using convolutional neural networks[J]. IEEE transactions on neural networks and learning systems, 2018, 29(11): 5619-5629.Google ScholarCross Ref
- Schirrmeister R T, Springenberg J T, Fiederer L D J, Deep learning with convolutional neural networks for EEG decoding and visualization[J]. Human brain mapping, 2017, 38(11): 5391-5420.Google Scholar
- Lawhern V J, Solon A J, Waytowich N R, EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces[J]. Journal of neural engineering, 2018, 15(5): 056013.Google Scholar
- Mane R, Robinson N, Vinod A P, A multi-view CNN with novel variance layer for motor imagery brain computer interface[C]//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2020: 2950-2953.Google Scholar
- Padfield N, Ren J, Qing C, Multi-segment majority voting decision fusion for MI EEG brain-computer interfacing[J]. Cognitive Computation, 2021, 13(6): 1484-1495.Google ScholarCross Ref
- Hu L, Xie J, Pan C, Multi-feature fusion method based on WOSF and MSE for four-class MI EEG identification[J]. Biomedical Signal Processing and Control, 2021, 69: 102907.Google ScholarCross Ref
Index Terms
- Motor imagery EEG decoding based on multi-loss fusion FBCNet
Recommendations
Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion
AbstractElectroencephalography (EEG) motor imagery (MI) signals have recently gained a lot of attention as these signals encode a person’s intent of performing an action. Researchers have used MI signals to help disabled persons, control ...
Highlights- Multi CNN models with different layers and filters for robust EEG feature extraction.
Correlation Between Forehead EEG and Sensorimotor Area EEG in Motor Imagery Task
DASC '09: Proceedings of the 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure ComputingElectrodes connection on the scalp needs to apply gel or paste on the scalp and fit EEG-cap on the head and this procedure also needs to deal with the hair on it. By comparison, to fit EEG electrodes on the forehead area is much easier because there is ...
Feature Analysis of EEG Based Brain-Computer Interfaces to Detect Motor Imagery
Brain InformaticsAbstractBrain-Computer Interfaces (BCI) is one of the alluring breakthroughs for mankind as it provides a new way of communication for the patients of neuro-muscular disorders. Electroencephalography (EEG) signals are the most studied type of signals to ...
Comments