Denoising of magnetoencephalographic data using spatial averaging
| Autor: | Srikantan S. Nagarajan, Kenneth E. Hild, Conor Heneghan, Stephen J. Redmond |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
medicine.diagnostic_test
business.industry Cognitive Neuroscience Noise reduction Speech recognition Pattern recognition Magnetoencephalography Filter (signal processing) Computer Science Applications Correlation Data set Functional brain Noise Artificial Intelligence medicine Artificial intelligence business Mathematics Communication channel |
| Zdroj: | Neurocomputing. 72:112-118 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2008.04.052 |
| Popis: | The study of human cognition, and preoperative functional brain mapping, are facilitated through the use of magnetoencephalography (MEG). However, the noise present in such recordings is significant relative to the signals of interest. To circumvent this issue multiple trials are performed for the same task and an ensemble average is performed to increase the signal-to-noise/interference ratio (SNIR). Unfortunately, large numbers of trials (100-500) are required to achieve a sufficiently large SNIR. This paper describes a simple denoising technique which employs spatial averaging to potentially reduce the number of required trials. The N trials from each of the 274 channels are first averaged. The 274 averaged channel estimates are then temporally low passed filtered, using a zero-phase filter. Finally, a spatial average is performed, where each channel is assigned the average value of a number of the most similar channels, including itself. The most similar channels are identified using correlation. The technique is applied to an auditory and somatosensory evoked response data set. A gain of approximately 1-1.5dB is observed over low-pass filtering alone. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect