electroencephalogram signal feature extraction and classification recognition method based on LSTM-FC

A feature extraction, EEG technology, applied in biometric recognition, neural learning methods, character and pattern recognition, etc., can solve the key information without considering the timing, the processing effect is unsatisfactory, and the loss of feature information.

Inactive Publication Date: 2019-04-05
QILU UNIV OF TECH +1
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AI Technical Summary

Problems solved by technology

Traditional machine learning algorithms generally require cumbersome feature engineering. At the same time, the key information of time series is not considered in the modeling of complex time series s

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  • electroencephalogram signal feature extraction and classification recognition method based on LSTM-FC
  • electroencephalogram signal feature extraction and classification recognition method based on LSTM-FC
  • electroencephalogram signal feature extraction and classification recognition method based on LSTM-FC

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Embodiment Construction

[0057] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are explanations of the present invention, but the present invention is not limited to the following embodiments.

[0058] Such as figure 1 As shown, a kind of LSTM-FC-based EEG feature extraction and classification recognition method provided by the present invention is characterized in that it comprises the following steps:

[0059] S1: Acquisition and preprocessing of EEG signals;

[0060] The data set used in this embodiment belongs to the ECoG data based on motor imagery, and adopts an intrusive way to collect EEG signals, such as figure 2 As shown, an 8×8 cm grid-shaped platinum electrode with a size of 8×8 was placed on the surface of the motor cortex of the right hemisphere of the subject’s brain. In the experiment, the subjects repeatedly imagined the two types of movements of sticking out the tongue and the ...

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Abstract

The invention relates to a method based on LSTM-. The electroencephalogram signal feature extraction and classification recognition method for the FC is characterized by comprising the following steps: S1, collecting and preprocessing electroencephalogram signals; S2, defining a basic model structure of the LSTM network; S3, enabling the feature matrix passing through the LSTM network to pass through a two-layer FC network to obtain an LSTM model fused with the FC network, namely, LSTM-; An FC model; S4, inputting the training set into a corresponding model for training, and updating the network by using error back propagation; S5, after the corresponding model is trained, inputting the test set into the model to obtain the final classification accuracy of the motor imagery task, and thenthe performance of the model is evaluated; S6: comparing performance of LSTM and LSTM-FC models to obtain the best model.

Description

technical field [0001] The invention belongs to the technical field of brain-computer interface of artificial intelligence, and relates to a brain-computer interface (Brain- The motor imagery classification and recognition method in the Computer Interface (BCI) system, especially a method for feature extraction and classification recognition of EEG signals based on LSTM-FC; this method uses an LSTM model in deep learning to extract The characteristics of the signal and the feature matrix of the information it carries, and then use the FC network to further fuse and extract the feature information extracted by LSTM, and finally map the extracted feature matrix to the sample label space to achieve end-to-end EEG signal classification identify. Background technique [0002] BCI is a communication system that can realize the interaction between the brain and external devices. It attempts to directly connect the internal neuron activity of the brain with external devices in orde...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/08
CPCG06N3/084G06V40/10G06F18/241
Inventor 徐舫舟许晓燕舒明雷
Owner QILU UNIV OF TECH
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