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Motor imagery EEG recognition with KNN-based smooth auto-encoder. | LitMetric

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

Artif Intell Med

Chongqing Key Laboratory of Complex Systems and Bionic Control, College of Automation, Chongqing University of Posts and Telecommunications, Nan'an district, Chongqing, 400065, China.

Published: November 2019

AI Article Synopsis

  • Brain-computer interfaces (BCIs) are becoming popular for improving communication between the brain and external devices, with EEG signals being a key focus in this field.
  • The study introduces a novel semi-supervised model called KNN-based smooth auto-encoder (k-SAE), which enhances data representation by using nearest neighbor values and reconstructing inputs, while also reducing noise.
  • Experimental results indicate that k-SAE effectively extracts features and classifies motor imaging EEG signals, achieving higher recognition accuracy compared to existing algorithms.

Article Abstract

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.

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Source
http://dx.doi.org/10.1016/j.artmed.2019.101747DOI Listing

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