Motor imagery EEG recognition with KNN-based smooth auto-encoder

• Published in: Artificial intelligence in medicine Vol. 101; p. 101747
• Main Authors: Tang, Xianlun, Wang, Ting, Du, Yiming, Dai, Yuyan
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-11-2019
• Subjects: BCI; EEG recognition; Feature extraction; KNN-based smooth auto-encoder; Motor imagery; BCI; Feature extraction; Motor imagery; EEG recognition; KNN-based smooth auto-encoder
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/j.artmed.2019.101747

•We devised a novel model, KNN-based smooth auto-encoder, to achieve accurate recognition of motor imaging EEG signals.•K-SAE construct a new input and learns the robust features representation by reconstructing this new input instead of the original input, which is different from the traditional automatic encoder (AE).•The Gaussian filter is selected as the convolution kernel function in k-SAE to smooth the noise in the feature.•The experiments in this paper select two sets of data for verifying the validity of the proposed method. One is obtained by EEG signal acquisition experiment and the other is public data set.
As new human-computer interaction technology, brain-computer interface has been widely used in various fields of life. The study of EEG signals cannot only improve people's awareness of the brain, but also establish new ways for the brain to communicate with the outside world. This paper takes the motion imaging EEG signal as the research object and proposes an innovative semi-supervised model called KNN-based smooth auto-encoder (k-SAE). K-SAE looks for the nearest neighbor values of the samples to construct a new input and learns the robust features representation by reconstructing this new input instead of the original input, which is different from the traditional automatic encoder (AE). The Gaussian filter is selected as the convolution kernel function in k-SAE to smooth the noise in the feature. Besides, the data information and spatial position of the feature map are recorded by max-pooling and unpooling, that help to prevent loss of important information. The method is applied to two data sets for feature extraction and classification experiments of motor imaging EEG signals. The experimental results show that k-SAE achieves good recognition accuracy and outperforms other state-of-the-art recognition algorithms.