چكيده به لاتين
In recent decades, the use of electroencephalogram (EEG) signals to communicate with the environment has led to the emergence of brain-computer interfaces (BCIs). The P300 signal is a type of brain potential used in BCI systems. In this study, visual stimulation is used to excite the P300 signal for the purpose of online control of the car. For this purpose, a graphical user interface (GUI) with seven keys is designed to control the car, which four keys are used to control the left, right, back and forward, one key to increase speed, one key to reduce speed and one key Start / Stop is designed to turn on the car and stop the car. The car is turned on with a beep sound, and the start / stop key can also be used to enter the car into control and uncontrolled mode (when the car is stopped, the GUI enters the uncontrolled mode and no command is sent to the car). The car used in this project was a control machine that after detaching its control board, the boards needed to connect to the computer and control by brain signals were used. After signal recording in the offline stage, various methods have been used to analyze the recorded data, including feature extraction methods with autoencoder neural networks (AE), principal component analysis (PCA), linear resolution analysis (LDA). And classification by backup vector machine (SVM) method has been used, convolutional neural networks (CNN) have been used for feature extraction as well as classification. Finally, the feature extraction method with two layer of autoencoder neural network algorithms and principal component analysis and output combination of these two methods and the use of SVM to classify these features were selected as the final method in the online mode. In online experiments, the classification accuracy for car motion control was determined that the car was controlled by users and the average online accuracy was 88.7 with an information rate of 30 bits per minute.
Keywords: Feature extraction, Principle Componenet Analysis (PCA), Support Vector machine (SVM)
