Abstract
In this work, we present a novel method to intention recognition, based on electroencephalogram (EEG) and eye movement in human-computer interaction(HCI). The fusion of EEG and eye movement will allow the utmost of the advantages of the two physiological signals. Signals of EEG and eye movement were collected for feature extraction, recognition network of machine learning pattern was input for intent recognition, final recognition result was attained by decision-level fusion. We compare the results of the Intention Recognition Algorithms to those of an experiment involving the intention recognition of the operator in a simulated flight mission. In most every case, results show that the intention recognition algorithms performed better than solely rely on single signal.
Highlights
One of the key targets of the human-computer interaction intelligence [1] is to improve the user’s intention perception derived from the human-computer interaction system
The specific implementation process is as follows: collecting EEG and eye movement signals of its user for feature extraction; using pattern recognition algorithms to classify and identify physiological signal features; performing decision-level fusion on the classifiable algorithm to obtain the final result, performing user intention-induced experiments to verify the feasibility of the method
Algorithm of CSP mode has proved to be effective in the analysis of EEG signals based on EDS/ERS, the CSP mode algorithm is proposed for the binary classification problem
Summary
One of the key targets of the human-computer interaction intelligence [1] is to improve the user’s intention perception derived from the human-computer interaction system. Amin et al [13] collect EEG signals, perform feature extraction and data classification to realize the remote control of UAV through BCI These intent recognition methods only rely on EEG signals without the advantages of integrating eye movement signals, of which recognition accuracy rate needs to be further improved. The specific implementation process is as follows: collecting EEG and eye movement signals of its user for feature extraction; using pattern recognition algorithms to classify and identify physiological signal features; performing decision-level fusion on the classifiable algorithm to obtain the final result, performing user intention-induced experiments to verify the feasibility of the method. The effect of different EEG feature extraction methods and the effect of different machine learning algorithms on recognition accuracy have been compared
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