EEG Cognitive Load Analysis via HHT Artifact Removal
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Solution Overview
Problem
Low cost, low resolution EEG devices struggle to accurately measure cognitive load due to limited channels and noise contamination, leading to subjective placement of sensitive EEG channels and inadequate noise removal, which affects the accuracy of cognitive load determination.
Innovation Solution
A method using a Hilbert-Huang Transform filter to preprocess EEG signals from a low resolution EEG device with up to 14 channels, extracting Fast Fourier Transform based alpha and theta band power features, and classifying them using a supervised machine learning technique to determine cognitive load, specifically focusing on signals from the left-frontal brain lobe.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If low cost low resolution EEG devices are used, then cost is reduced, but measurement precision deteriorates due to fewer EEG channels and noise contamination
Solution Approach 1:
The patent extracts and removes noise components from EEG signals using signal processing techniques. Specifically, it separates and eliminates artifacts such as eye movements, muscle activity, and electrical interference from the recorded EEG data, retaining only the relevant neural signals for cognitive load analysis
Solution Approach 2:
The patent introduces intermediate processing steps including signal filtering, artifact removal algorithms, and feature extraction methods that act as mediators between the raw EEG data and cognitive load measurement. These intermediate processes enhance the quality of data from low-resolution devices
2Device complexity
If low cost low resolution EEG devices with fewer channels are used, then device complexity is reduced, but reliability deteriorates due to missing sensitive EEG channels and inability to remove noise
Solution Approach 1:
The patent creates a virtual representation of additional EEG channels through signal processing and computational methods. By analyzing patterns from available channels and using algorithms to reconstruct or estimate signals that would be present with more channels, it compensates for the limited physical channel count
Solution Approach 2:
The patent transforms the EEG data by changing parameters such as frequency domain representation, time-frequency analysis, and feature space transformation. These parameter changes enable reliable cognitive load measurement by emphasizing relevant signal characteristics while suppressing noise
3Ease of operation
If standard EEG processing methods are used without specialized filtering, then processing simplicity is maintained, but measurement precision deteriorates due to noise contamination from non-cerebral artifacts
Solution Approach 1:
The patent applies preliminary signal processing and artifact removal techniques before main analysis. By pre-processing the EEG data to remove known artifacts and noise sources beforehand, it simplifies subsequent analysis while improving measurement precision
Solution Approach 2:
The patent converts the challenge of noise contamination into an opportunity by using the noise characteristics themselves for identification and removal. By analyzing artifact patterns and using them as reference signals, the system effectively eliminates their harmful effects
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively removes non-cerebral artifacts and reduces computational complexity, enabling accurate cognitive load measurement with improved separation between high and low cognitive load tasks, even with limited EEG channels, and enhances the accuracy of cognitive load determination.
Implementation Method 1
preprocessing, by the processor, the EEG signals using a Hilbert-Huang Transform (HHT) filter to remove a noise corresponding to one or more unrelated, non-cerebral artifacts
Implementation Method 2
extracting, by the processor, features comprising Fast Fourier Transform (FFT) based alpha and theta band power, from the preprocessed EEG signals
Data Source
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AI summary
Disclosed is a method and system for determining a cognitive load of a subject from Electroencephalography (EEG) signals. EEG signals are received from EEG channels associated with a left-frontal brain lobe. EEG signals are associated with a subject performing cognitive task. EEG signals are received from a low resolution EEG device. EEG channels comprise four EEG channels associated with the left-frontal brain lobe. EEG signals are preprocessed using a Hilbert-Huang Transform (HHT) filter to remove a noise corresponding to one or more non-cerebral artifacts to generate preprocessed EEG signals. Features comprising Fast Fourier Transform (FFT) based alpha and theta band power are extracted from the preprocessed EEG signals. Feature vector is generated from the features. The feature vector is classified using a Support Vector Machine (SVM) classifier to determine the cognitive load of the subject.