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Imaginary Control of a Mobile Vehicle Using Deep Learning Algorithm: A Brain Computer Interface Study
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DOI：10.1109/ACCESS.2021.3128611.png)
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Controlling a remote mobile vehicle using electroencephalograph (EEG) signals is still a challenge specially achieving a high degree of accuracy and precision. In the present study, the focus is on implementing an efficient feature space in a deep-based learning (DL) algorithm for a single trial application. More specifically, a boosting feature algorithm by means of long short-term memory (LSTM) networks are implemented in a deep auto-encoder (DAE) algorithm for producing an effective feature space tp identify event related desynchronization/event related synchronization (ERD/ERS) patters in EEG signals. For this purpose, three different DL-based algorithms are implemented that the models are based on a convolutional neural network (CNN), DAE, and LSTM networks to extract and boost the main features. In addition, our previous improved support vector machine (SVM)-based algorithm is employed to consider the potential of SVM and implemented DL-based algorithms for a two classes identification. To consider the efficiency of our implemented methods, algorithms are employed for control of a remote mobile vehicle in an imaginary right-hand opening and making a right-hand fist task. In our experiment, eleven subjects participated in an imaginary movement task. In the experiment, the displayed movement pictures were colored in yellow and red colors for stimulating brain to generate stronger ERD/ERS patterns. Results showed that the proposed algorithm by using the boosting technique significantly increased the accuracy with a higher precision of 73.31% +/- 0.03. The proposed method enabled the DL algorithm to be used in single trial experiments.
Keyword:
Feature extraction
Electroencephalography
Signal processing algorithms
Support vector machines
Task analysis
Optimization
Neurons
Brain-computer interface
EEG signal processing
event related desynchronization
deep learning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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