Abstract
The classification recognition rate of motor imagery is a key factor to improve the performance of brain–computer interface (BCI). Thus, we propose a feature extraction method based on discrete wavelet transform (DWT), empirical mode decomposition (EMD), and approximate entropy. Firstly, the electroencephalogram (EEG) signal is decomposed into a series of narrow band signals with DWT, then the sub-band signal is decomposed with EMD to get a set of stationary time series, which are called intrinsic mode functions (IMFs). Secondly, the appropriate IMFs for signal reconstruction are selected. Thus, the approximate entropy of the reconstructed signal can be obtained as the corresponding feature vector. Finally, support vector machine (SVM) is used to perform the classification. The proposed method solves the problem of wide frequency band coverage during EMD and further improves the classification accuracy of EEG signal motion imaging.
Highlights
Brain–computer interface (BCI) is a communication control system that does not rely on the normal output channels of muscles and the nerves around the brain
Mu and beta rhythms are enhanced with event-related synchronization (ERS) during this process whereas these two rhythms are declined, which is labeled as event-related desynchronization (ERD) [20]
wavelet transform (WT) is performed prior to empirical mode decomposition (EMD) to split the signal into a set of narrowband signals, and the appropriate sub-band signal is selected to decompose it into an intrinsic mode function with a more concentrated frequency
Summary
Brain–computer interface (BCI) is a communication control system that does not rely on the normal output channels of muscles and the nerves around the brain. BCI bypasses external nerves and muscle tissue, directly establishing information channels between brain consciousness and external devices [1], avoiding nerve conduction and muscle movement, providing a new means of rehabilitation for patients with nerve damage or muscle damage so that they do not have to rely on others to complete their own exercises [2]. The feature signals extracted by the feature can identify the discrimination information of different imaginary motion electroencephalogram (EEG) signals. It has a great influence on the subsequent classification and recognition, so feature extraction has received extensive attention in the BCI research community
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