Extraction of the mismatch negativity elicited by sound duration decrements: A comparison of three procedures

Kalyakin, Igor; González, Narciso Barrero; Ivannikov, Andriy; Lyytinen, Heikki

doi:10.1016/j.datak.2009.07.004

Cited by 12 publications

(14 citation statements)

References 22 publications

(59 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is also the default of the set in ICASSO software. Kalyakin et al (2008Kalyakin et al ( , 2009) discussed this software on the application to the averaged EEG recordings in details. We used the same decomposition algorithm in this study.…”

Section: Estimating Erp Component With Icassomentioning

confidence: 99%

“…This number in our dataset did not match the necessity of ICA. To facilitate ICA, Kalyakin et al (2008Kalyakin et al ( , 2009) concatenated the averaged traces of different deviants at each channel for each subject. We repeated this procedure here.…”

Section: Estimating Erp Component With Icassomentioning

confidence: 99%

“…wICA Averaged trace for wICA MMN has been extracted by ICA from the recordings of concatenated single trials (Marco-Pallares et al 2005), and from the averaged recordings over single trials (Kalyakin et al 2009). The former paradigm supplies more samples for ICA decomposition and benefits the convergence of the ICA algorithm; however, the latter procedure provides ICA with better structured MMN waveform for the decomposition.…”

Section: Eeg Recordingsmentioning

confidence: 99%

“…They named the software as ICASSO (Himberg et al 2004). It has been used to estimate the MMN component by Kalyakin et al (2008Kalyakin et al ( , 2009.…”

Section: Estimating Erp Component With Icassomentioning

confidence: 99%

“…Although ICA has been extensively used to study ERPs (Vigario and Oja 2008; Delorme and Makeig 2004), in the research of a one of the most interesting ERPs, viz, mismatch negativity (MMN) (Näätänen et al 1978;Näätänen 1992;Duncan et al 2009), only few studies have reported the application of ICA to extract MMN (Kalyakin et al 2009;Kalyakin et al 2008;Marco-Pallares et al 2005). MMN studies are usually based on the average over hundreds of single trials of EEG recordings (Näätänen et al 1978;Näätänen 1992;Duncan et al 2009).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Answering six questions in extracting children’s mismatch negativity through combining wavelet decomposition and independent component analysis

Cong

Kalyakin

et al. 2011

Cogn Neurodyn

Self Cite

View full text Add to dashboard Cite

This study combines wavelet decomposition and independent component analysis (ICA) to extract mismatch negativity (MMN) from electroencephalography (EEG) recordings. As MMN is a small event-related potential (ERP), a systematic ICA based approach is designed, exploiting MMN's temporal, frequency and spatial information. Moreover, this study answers which type of EEG recordings is more appropriate for ICA to extract MMN, what kind of the preprocessing is beneficial for ICA decomposition, which algorithm of ICA can be chosen to decompose EEG recordings under the selected type, how to determine the desired independent component extracted by ICA, how to improve the accuracy of the back projection of the selected independent component in the electrode field, and what can be finally obtained with the application of ICA. Results showed that the proposed method extracted MMN with better properties than those estimated by difference wave only using temporal information or ICA only using spatial information. The better properties mean that the deviant with larger magnitude of deviance to repeated stimuli in the oddball paradigm can elicit MMN with larger peak amplitude and shorter latency. As other ERPs also have the similar information exploited here, the proposed method can be used to study other ERPs.

show abstract

Section: Estimating Erp Component With Icassomentioning

confidence: 99%

Section: Estimating Erp Component With Icassomentioning

confidence: 99%

Section: Eeg Recordingsmentioning

confidence: 99%

“…They named the software as ICASSO (Himberg et al 2004). It has been used to estimate the MMN component by Kalyakin et al (2008Kalyakin et al ( , 2009.…”

Section: Estimating Erp Component With Icassomentioning

confidence: 99%