Blind source separation for spike sorting of high density microelectrode array recordings

2011 ◽  
Author(s):  
David Jackel ◽  
Urs Frey ◽  
Michele Fiscella ◽  
Andreas Hierlemann
Keyword(s):  
Blind Source Separation ◽  
Microelectrode Array ◽  
Source Separation ◽  
High Density ◽  
Spike Sorting ◽  
Array Recordings

scholarly journals Applicability of independent component analysis on high-density microelectrode array recordings

2012 ◽  
Vol 108 (1) ◽  
pp. 334-348 ◽  
Author(s):  
David Jäckel ◽  
Urs Frey ◽  
Michele Fiscella ◽  
Felix Franke ◽  
Andreas Hierlemann
Keyword(s):  
Microelectrode Array ◽  
Simulated Data ◽  
Complementary Metal Oxide Semiconductor ◽  
Simultaneous Recording ◽  
High Density ◽  
Independent Component ◽  
Oxide Semiconductor ◽  
Spike Sorting ◽  
Spatiotemporal Resolution ◽  
Source Signals

Emerging complementary metal oxide semiconductor (CMOS)-based, high-density microelectrode array (HD-MEA) devices provide high spatial resolution at subcellular level and a large number of readout channels. These devices allow for simultaneous recording of extracellular activity of a large number of neurons with every neuron being detected by multiple electrodes. To analyze the recorded signals, spiking events have to be assigned to individual neurons, a process referred to as “spike sorting.” For a set of observed signals, which constitute a linear mixture of a set of source signals, independent component (IC) analysis (ICA) can be used to demix blindly the data and extract the individual source signals. This technique offers great potential to alleviate the problem of spike sorting in HD-MEA recordings, as it represents an unsupervised method to separate the neuronal sources. The separated sources or ICs then constitute estimates of single-neuron signals, and threshold detection on the ICs yields the sorted spike times. However, it is unknown to what extent extracellular neuronal recordings meet the requirements of ICA. In this paper, we evaluate the applicability of ICA to spike sorting of HD-MEA recordings. The analysis of extracellular neuronal signals, recorded at high spatiotemporal resolution, reveals that the recorded data cannot be modeled as a purely linear mixture. As a consequence, ICA fails to separate completely the neuronal signals and cannot be used as a stand-alone method for spike sorting in HD-MEA recordings. We assessed the demixing performance of ICA using simulated data sets and found that the performance strongly depends on neuronal density and spike amplitude. Furthermore, we show how postprocessing techniques can be used to overcome the most severe limitations of ICA. In combination with these postprocessing techniques, ICA represents a viable method to facilitate rapid spike sorting of multidimensional neuronal recordings.

scholarly journals Estimating the fraction of falsely detected spikes in high density microelectrode array recordings based on correlations

2013 ◽  
Vol 14 (S1) ◽  
Author(s):  
Jens-Oliver Muthmann ◽  
Hayder Amin ◽  
Alessandro Maccione ◽  
Evelyne Sernagor ◽  
Luca Berdondini ◽  
...  
Keyword(s):  
Microelectrode Array ◽  
High Density ◽  
Array Recordings

scholarly journals A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data

2021 ◽  
Author(s):  
Mingqi Zhao ◽  
Gaia Bonassi ◽  
Roberto Guarnieri ◽  
Elisa Pelosin ◽  
Alice Nieuwboer ◽  
...  
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Event Related Potentials ◽  
Auditory Stimulation ◽  
Single Step ◽  
High Density ◽  
Artifact Removal ◽  
Source Imaging ◽  
Study Results ◽  
Related Potentials

Abstract Objective. Electroencephalography (EEG) is a widely used technique to address research questions about brain functioning, from controlled laboratorial conditions to naturalistic environments. However, EEG data are affected by biological (e.g., ocular, myogenic) and non-biological (e.g., movement-related) artifacts, which -depending on their extent- may limit the interpretability of the study results. Blind source separation (BSS) approaches have demonstrated to be particularly promising for attenuation of artifacts in high-density EEG (hdEEG) data. Previous EEG artifact removal studies suggested that it may not be optimal to use the same BSS method for different kinds of artifacts. Approach. In this study, we developed a novel multi-step BSS approach to optimize the attenuation of ocular, movement-related and myogenic artifacts from hdEEG data. For validation purposes, we used hdEEG data collected in a group of healthy participants in standing, slow-walking and fast-walking conditions. During part of the experiment, a series of tone bursts were used to evoke auditory responses. We quantified event-related potentials (ERPs) using hdEEG signals collected during auditory stimulation, as well as event-related desynchronization (ERD) by contrasting hdEEG signals collected in walking and standing conditions, without auditory stimulation. We compared the results obtained in terms of auditory ERP and motor-related ERD using the proposed multi-step BSS approach, with respect to two classically used single-step BSS approaches. Main results. The use of our approach yielded the lowest residual noise in the hdEEG data, and permitted to retrieve stronger and more reliable modulations of neural activity than alternative solutions. Overall, our study confirmed that the performance of BSS-based artifact removal can be improved by using specific BSS methods and parameters for different kinds of artifacts. Significance. Our technological solution supports a wider use of hdEEG-based source imaging in movement and rehabilitation studies, and contribute to further development of mobile brain/body imaging applications.

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