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Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays
Gonzalo E Mena1, Lauren E Grosberg2, Sasidhar Madugula2
1Statistics Department, Columbia University, New York, New York, United States of America.
Plos Computational Biology
|November 14, 2017
Summary
This study presents a new algorithm for analyzing neural recordings during electrical stimulation. The method accurately separates stimulation artifacts from neural signals, improving spike sorting for brain-computer interfaces.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Simultaneous electrical stimulation and recording with multi-electrode arrays are crucial for understanding neural circuits and developing neural interfaces.
- Interpreting these recordings is difficult due to complex electrical stimulation artifacts that interfere with spike sorting.
Purpose of the Study:
- To develop a scalable algorithm for artifact estimation and evoked spike identification in multi-electrode neural recordings.
- To improve the accuracy and efficiency of spike sorting in the presence of stimulation artifacts.
Main Methods:
- A structured Gaussian Process model was developed to estimate and remove electrical stimulation artifacts.
- The algorithm was tested on real and simulated 512-electrode recordings from primate retinas with various stimulation types.
Main Results:
- The algorithm demonstrated low error rates in identifying evoked spikes.
- The computational complexity is suitable for real-time data analysis.
Conclusions:
- This scalable algorithm effectively distinguishes neural signals from stimulation artifacts, enhancing spike sorting.
- The technology has potential applications in designing advanced sensory prostheses, such as retinal prostheses, and enabling large-scale closed-loop neural stimulation.
