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Automatic bad channel detection in implantable brain-computer interfaces using multimodal features based on local
Mengmeng Li1, You Liang1, Lifang Yang1
1School of Electrical Engineering, Zhengzhou University, Zhengzhou, Henan, China; Industrial Technology Research Institute, Zhengzhou University, Zhengzhou, Henan, China.
Computers in Biology and Medicine
|February 1, 2020
Summary
This study introduces a machine learning method to automatically detect faulty channels in neural recordings using combined local field potential (LFP) and spike signal features. The approach enhances data quality for large-scale neural signal analysis.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Signal Processing
Background:
- Implantable multi-channel neural recordings are crucial for understanding brain function.
- Identifying "bad channels" is essential for accurate neural signal analysis, especially in big data scenarios.
- Current methods for bad channel detection are often insufficient for complex datasets.
Purpose of the Study:
- To develop and validate an automated method for detecting bad channels in multi-channel neural recordings.
- To leverage multimodal features from local field potentials (LFPs) and spike signals for improved detection accuracy.
- To assess the performance of various machine learning classifiers in identifying faulty neural channels.
Main Methods:
- Combined local field potentials (LFPs) and spike signals from 2632 recordings in pigeons.
- Extracted 12 multimodal features quantifying temporal, frequency, phase, and firing-rate properties.
- Employed machine learning classifiers, including Random Forests, with synthetic minority oversampling technique (SMOTE) and Fisher weighted Euclidean distance sorting (FWEDS) to handle class imbalance.
Main Results:
- Correlation coefficient, phase locking value, and coherence demonstrated strong discriminability for bad channels.
- Post-SMOTE operation, most classifiers achieved high accuracy and bad channel detection rates.
- Random Forests classifier exhibited superior comprehensive performance with accuracy (0.9092 ± 0.0081), precision (0.9123 ± 0.0100), and recall (0.9057 ± 0.0121).
Conclusions:
- The proposed multimodal feature-based machine learning approach effectively automates bad channel detection in neural recordings.
- This method provides a valuable tool for improving the reliability and efficiency of analyzing large neural datasets.
- The findings offer practical insights for researchers dealing with noisy or artifact-laden neural data.
