Abstract
Recognition of motor imagery intention is one of the hot current research focuses of brain-computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their movement intentions. In recent years, breakthroughs have been made in the research on recognition of motor imagery task using deep learning, but if the important features related to motor imagery are ignored, it may lead to a decline in the recognition performance of the algorithm. This paper proposes a new deep multi-view feature learning method for the classification task of motor imagery electroencephalogram (EEG) signals. In order to obtain more representative motor imagery features in EEG signals, we introduced a multi-view feature representation based on the characteristics of EEG signals and the differences between different features. Different feature extraction methods were used to respectively extract the time domain, frequency domain, time-frequency domain and spatial features of EEG signals, so as to made them cooperate and complement. Then, the deep restricted Boltzmann machine (RBM) network improved by t-distributed stochastic neighbor embedding(t-SNE) was adopted to learn the multi-view features of EEG signals, so that the algorithm removed the feature redundancy while took into account the global characteristics in the multi-view feature sequence, reduced the dimension of the multi-visual features and enhanced the recognizability of the features. Finally, support vector machine (SVM) was chosen to classify deep multi-view features. Applying our proposed method to the BCI competition IV 2a dataset we obtained excellent classification results. The results show that the deep multi-view feature learning method further improved the classification accuracy of motor imagery tasks.
Highlights
EEG signals are spontaneous potential activities generated by brain nerve activity and contain abundant brain activity information [1]
The comparison results show that, on the datasets provided by most subjects, the features extracted using the deep multi-view feature learning method proposed in this paper are superior to other comparison methods in the classification effect, but referring to the comprehensive evaluation of the designed method, we will discuss it from the following aspects
A feature learning method based on deep multi-view was proposed, and this method was applied to the recognition of motor imagery tasks of EEG signals
Summary
EEG signals are spontaneous potential activities generated by brain nerve activity and contain abundant brain activity information [1]. The current research on the brain is mainly based on the analysis of EEG signals. The BCI is the key for the human brain to communicate with the outside world. It is a non-muscle channel communication method [2,3,4]. Through the non-invasive BCI, we can obtain various patterns of brain activity signals, which are extensively studied and used in signal processing, pattern recognition, cognitive science, medicine, rehabilitation and other fields [7,8]. The analysis of EEG signals can sometimes help patients who cannot act autonomously due to injury to the muscles or nerves that control limb movement, such as strokes, spine injuries, craniocerebral nerve injuries, Sensors 2020, 20, 3496; doi:10.3390/s20123496 www.mdpi.com/journal/sensors
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