Classification of Motor Imagery EEG Signal for Navigation of Brain Controlled Drones
12 Dec 2019-pp 3-12
TL;DR: The performance of convolutional stacked autoencoder and Convolutional Long short term memory models for classification of Motor imagery EEG signal and the performance of these models have been compared with different machine learning models.
Abstract: Navigation of drones can be conceivably performed by operators by analyzing the brain signals of the person. EEG signal corresponding to the motor imaginations can be used for generation of control signals for drone. Different machine learning and deep learning approaches have been developed in the state of the art literature for the classification of motor imagery EEG signal. There is still a need for developing a suitable model that can classify the motor imagery signal fast and can generate a navigation command for drone in real-time. In this paper, we have reported the performance of convolutional stacked autoencoder and Convolutional Long short term memory models for classification of Motor imagery EEG signal. The developed models have been optimized using TensorRT that speeds up inference performance and the inference engine has been deployed on Jetson TX2 embedded platform. The performance of these models have been compared with different machine learning models.
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TL;DR: In this article , an embedded multi-tasks classifier based on motor imagery using the EEGNet network integrated into the NVIDIA Jetson TX2 card was implemented to classify discriminant channel signals.
Abstract: Nowadays, Brain–Computer Interfaces (BCIs) still captivate large interest because of multiple advantages offered in numerous domains, explicitly assisting people with motor disabilities in communicating with the surrounding environment. However, challenges of portability, instantaneous processing time, and accurate data processing remain for numerous BCI system setups. This work implements an embedded multi-tasks classifier based on motor imagery using the EEGNet network integrated into the NVIDIA Jetson TX2 card. Therefore, two strategies are developed to select the most discriminant channels. The former uses the accuracy based-classifier criterion, while the latter evaluates electrode mutual information to form discriminant channel subsets. Next, the EEGNet network is implemented to classify discriminant channel signals. Additionally, a cyclic learning algorithm is implemented at the software level to accelerate the model learning convergence and fully profit from the NJT2 hardware resources. Finally, motor imagery Electroencephalogram (EEG) signals provided by HaLT’s public benchmark were used, in addition to the k-fold cross-validation method. Average accuracies of 83.7% and 81.3% were achieved by classifying EEG signals per subject and motor imagery task, respectively. Each task was processed with an average latency of 48.7 ms. This framework offers an alternative for online EEG-BCI systems’ requirements, dealing with short processing times and reliable classification accuracy.
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Proceedings Article•
17 Jul 2017
TL;DR: This work introduces a new algorithm named WGAN, an alternative to traditional GAN training that can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches.
Abstract: We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches. Furthermore, we show that the corresponding optimization problem is sound, and provide extensive theoretical work highlighting the deep connections to different distances between distributions.
5,667 citations
Book•
24 Jan 2012TL;DR: The Future of BCIs: Meeting the Expectations Jonathan R. Wolpaw and Elizabeth Winter Wolpaws Index finds that BCI Therapeutic Applications for Improving Brain Function and Ethical Issues in BCI Research are becoming more important than ever.
Abstract: Contributors PART I: INTRODUCTION 1. Brain-Computer Interfaces: Something New under the Sun Jonathan R. Wolpaw and Elizabeth Winter Wolpaw PART II: BRAIN SIGNALS FOR BCIs 2. Neuronal Activity in Motor Cortex and Related Areas Lee E. Miller and Nicholas Hatsopoulos 3. Electric and Magnetic Fields Produced by the Brain Paul L. Nunez 4. Signals Reflecting Brain Metabolic Activity Nick F. Ramsey PART III: BCI DESIGN, IMPLEMENTATION, AND OPERATION 5. Acquiring Brain Signals from Within the Brain Kevin Otto, Kip A. Ludwig, Daryl R. Kipke 6. Acquiring Brain Signals from Outside the Brain Ramesh Srinivasan 7. BCI Signal Processing: Feature Extraction Dean J. Krusienski, Dennis J. McFarland, and Jose C. Principe 8. BCI Signal Processing: Feature Translation Dennis J. McFarland and Dean J. Krusienski 9. BCI Hardware and Software J. Adam Wilson, Christoph Guger, and Gerwin Schalk 10. BCI Operating Protocols Steven G. Mason, Brendan Z. Allison, and Jonathan R. Wolpaw 11. BCI Applications Jane E. Huggins and Debra Zeitlin PART IV: EXISTING BCIs 12. BCIs that Use P300 Event-Related Potentials Eric W. Sellers, Yael Arbel, and Emanuel Donchin 13. BCIs that Use Sensorimotor Rhythms Gert Pfurtscheller and Dennis J. McFarland 14. BCIs that Use Steady-State Visual Evoked Potentials or Slow Cortical Potentials Brendan Z. Allison, Josef Faller, and Christa Neuper 15. BCIs that Use Electrocorticographic (ECoG) Activity Gerwin Schalk 16. BCIs that Use Signals Recorded in Motor Cortex John P. Donoghue 17. BCIs that Use Signals Recorded in Parietal or Premotor Cortex Hansjorg Scherberger 18. BCIs that Use Brain Metabolic Signals Ranganatha Sitaram, Sangkyung Lee, and Niels Birbaumer PART V: USING BCIs 19. BCI Users and Their Needs Leigh R. Hochberg and Kim D. Anderson 20. Clinical Evaluation of BCIs Theresa M. Vaughan, Eric W. Sellers, and Jonathan R. Wolpaw 21. Dissemination: Getting BCIs to the People Who Need Them Frances J.R. Richmond and Gerald E. Loeb 22. BCI Therapeutic Applications for Improving Brain Function Janis J. Daly and Ranganatha Sitaram 23. BCI Applications for the General Population Benjamin Blankertz, Michael Tangermann, and Klaus-Robert Mu?ller 24. Ethical Issues in BCI Research Mary-Jane Schneider, Joseph J. Fins, and Jonathan R. Wolpaw PART VI: CONCLUSION 25. The Future of BCIs: Meeting the Expectations Jonathan R. Wolpaw and Elizabeth Winter Wolpaw Index
760 citations
TL;DR: The results show that deep learning methods provide better classification performance compared to other state of art approaches and can be applied successfully to BCI systems where the amount of data is large due to daily recording.
Abstract: Objective. Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. Approach. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. Main results. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Significance. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.
659 citations
Proceedings Article•
01 Jan 2016TL;DR: In this paper, a deep recurrent convolutional network was proposed to learn robust representations from multi-channel EEG time-series, and demonstrated its advantages in the context of mental load classification task.
Abstract: One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data. Herein, we propose a novel approach for learning such representations from multi-channel EEG time-series, and demonstrate its advantages in the context of mental load classification task. First, we transform EEG activities into a sequence of topology-preserving multi-spectral images, as opposed to standard EEG analysis techniques that ignore such spatial information. Next, we train a deep recurrent-convolutional network inspired by state-of-the-art video classification to learn robust representations from the sequence of images. The proposed approach is designed to preserve the spatial, spectral, and temporal structure of EEG which leads to finding features that are less sensitive to variations and distortions within each dimension. Empirical evaluation on the cognitive load classification task demonstrated significant improvements in classification accuracy over current state-of-the-art approaches in this field.
456 citations
23 Jan 2012
439 citations