Intelligent wheelchair control method and system based on human brain movement intention
A motion intention and control method technology, which is used in vehicle rescue, patient chairs or special transportation tools, medical transportation, etc., can solve the problem of potential interference corresponding to motion intention, inaccurate EEG signal processing, and poor real-time wheelchair control. and other problems, to ensure the concentration of attention, avoid poor real-time performance, not easy to noise and baseline interference
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Embodiment 1
[0041] A kind of intelligent wheelchair control method based on human brain motion intention, comprises the following steps:
[0042] Step 1: Construct the EEG brain network through the collected EEG signals;
[0043] Step 2: Classify the state of attention according to the node characteristics of the EEG brain network, and judge whether the subject is in a state of concentration. If so, start the wheelchair walking mode and skip to step 3; if not, skip to step 1;
[0044] Step 3: Extract the motor readiness potential, ERD features and eye-closing rhythm features of the collected EEG signals, and control the wheelchair according to the instructions of turning left, turning right, going straight and stopping going straight.
[0045] An intelligent wheelchair control system based on human brain motion intention, including an acquisition and amplification unit, a wireless transmission unit, an analysis unit and an intelligent wheelchair, wherein
[0046] The acquisition and ampl...
Embodiment 2
[0051] Step 1.1: After using the electrode cap to collect the EEG signal, use a notch filter to remove the power frequency interference on the EEG signal, use the template matching method to eliminate the oculoelectric artifact, and use the band-pass filter to remove the motion artifact to obtain the preprocessed EEG signal;
[0052] Step 1.2: Obtain EEG data based on the preprocessed EEG signal, define the electrode leads of the EEG data as the nodes of the EEG brain network, and define the coherence coefficient calculated based on the EEG data between the electrode pairs as the edge of the EEG brain network to complete Construct EEG brain network;
[0053] Step 2.1: Construct a weighted network according to the edges and nodes of the EEG brain network to calculate the node degree, and use the support vector machine classifier to classify the attention state with the node degree feature to judge whether it is a state of high concentration of attention. If so, skip to step 2.2 ...
Embodiment 3
[0059] Use dry electrodes to collect EEG signals and wirelessly transmit them to the analysis unit via Bluetooth; in the actual implementation, the electrode cap with 32 dry electrodes is worn on the patient's head, and the electrodes are arranged according to the international standard 10-20 standard. The weak EEG signals recorded by the 32 conductive electrodes are converted into digital signals after being amplified by the amplifier, and wirelessly transmitted to the computer equipped with the analysis unit through the bluetooth interface.
[0060] Construct the EEG brain network after preprocessing the collected EEG signals: In the specific implementation, the notch filter is used to filter out the power frequency interference in the EEG signals, and the template matching method is used to eliminate the ocular artifacts in the EEG signals. The band-pass filter method removes the motion artifacts in the EEG signal; the EEG data is obtained based on the preprocessed EEG signa...
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