EEG Signal Processing for Time-Node Action Intention Prediction
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Solution Overview
Problem
Existing neural network models for processing electroencephalogram signals analyze the signals as a whole, leading to suboptimal accuracy in action intention prediction.
Innovation Solution
The method involves dividing electroencephalogram signals into segments based on a preset time interval, predicting action intentions at multiple time nodes, and determining a target action intention using a combination of feature extraction and machine learning techniques, including convolutional neural networks and long short-term memory networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network models process electroencephalogram signals as a whole, then the processing is simple, but the accuracy of analysis results deteriorates
Solution Approach 1:
The patent divides the electroencephalogram signal into multiple signal segments based on time nodes, where each segment corresponds to a specific time interval. This segmentation allows the model to analyze local temporal patterns and transitions separately, improving prediction accuracy by capturing fine-grained temporal dynamics that would be lost in whole-signal processing.
2Measurement precision
If multiple time nodes are used for prediction, then the accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent segments the signal processing task into multiple independent time node predictions, where each time node is processed separately through the neural network. This approach enables parallel computation of multiple predictions, improving overall accuracy while managing computational load through efficient resource utilization across multiple prediction points.
Solution Approach 2:
The patent performs predictions at multiple time nodes within the signal segment, using more prediction points than the minimum single-point prediction. This excessive action of predicting at multiple locations provides redundant information that improves accuracy through aggregation, while the computational overhead is justified by the significant improvement in prediction reliability.
Data Source
AI summary
Provided is a method for processing an electroencephalogram signal executed by a computer device. The method includes acquiring an electroencephalogram signal, the electroencephalogram signal comprising an electroencephalogram signal data segment of a specified duration; determining a plurality of time nodes in the electroencephalogram signal data segment according to a preset time interval; predicting an action intention corresponding to each time node based on the electroencephalogram signal data segment to obtain action intention prediction results respectively corresponding to the plurality of time nodes; and determining a target action intention according to the action intention prediction results.