Abstract:
:The classical approach for extracting event-related potentials (ERPs) from the brain is ensemble averaging. For long latency ERPs this is not optimal, partly due to the time-delay in obtaining a response and partly because the latency and amplitude for the ERP components, like the P300, are variable and depend on cognitive function. This study compares the performance of a simplified Kalman filter with Sliding Window Averaging in tracking dynamical changes in single trial P300. The comparison is performed on simulated P300 data with added background noise consisting of both simulated and real background EEG in various input signal to noise ratios. While both methods can be applied to track dynamical changes, the simplified Kalman filter has an advantage over the Sliding Window Averaging, most notable in a better noise suppression when both are optimized for faster changing latency and amplitude in the P300 component and in a considerably higher robustness towards suboptimal settings. The latter is of great importance in a clinical setting where the optimal setting cannot be determined.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Vedel-Larsen E,Fuglø J,Channir F,Thomsen CE,Sørensen HBdoi
10.1016/j.cmpb.2009.12.006subject
Has Abstractpub_date
2010-09-01 00:00:00pages
252-60issue
3eissn
0169-2607issn
1872-7565pii
S0169-2607(09)00309-5journal_volume
99pub_type
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journal_title:Computer methods and programs in biomedicine
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journal_title:Computer methods and programs in biomedicine
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journal_title:Computer methods and programs in biomedicine
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journal_title:Computer methods and programs in biomedicine
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journal_title:Computer methods and programs in biomedicine
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journal_title:Computer methods and programs in biomedicine
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journal_title:Computer methods and programs in biomedicine
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journal_title:Computer methods and programs in biomedicine
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