Greetings RG community,
I have been working on a pipeline for the classification of EEG motor imagery signals. This is currently being done on the Giga Science MI dataset with 52 subjects, using all 64 EEG channels. The question can be isolated to my first preprocessing step involving ICA by way of Hyvarinen's fast fixed-point algorithm. If I develop spatial filters (vectors of the unmixing matrix) using only the intended training data, is it a violation of appropriate protocol if I then project all raw data (which includes testing data) on these vectors in an attempt at blind source separation? The nuanced thing that brings about concerns is that the raw data is provided in two matrices each containing LH+RS and RH+RS signals (LH = left hand; RH = right hand; and RS = resting state). If the spatial filters wL and wR were constructed using the LH and RH training data respectively, then the original raw data of the LH and RH matrices (including both training and testing data) were projected into these directions prior to the partitioning of trials, is this considered using knowledge of the classes thereby rendering the entire analysis ineffective? At first, I thought I was in the clear because class labels were not used as ICA is unsupervised, now I think it pertinent to ask someone that may have experience in this field.
My results were great under these conditions (perhaps this would be obvious). To check if I could replicate results a different way, I vertically concatenated the LH and RH training data (doubling the number of samples in comparison to the conditions described above) and developed a single matrix of spatial filters then projected all original data onto these but the results were poor, indicating a large drop in spatial resolution. Ideally, I would like to develop a single set of spatial filters that can be applied to all data indiscriminately, if anyone has any advice given the situation it would be greatly appreciated. Since this step is being done prior to the partitioning of MI trials, I have considered performing ICA on some vertically concatenated trials after partitioning and was wondering if this would yield good results as I have also read that the resting state signals contain important information for the minimization of mutual information (maximization of differential entropy), so I am uncertain with this approach. I am also in the process of replacing FastICA with SOBI, JADE, and infomax in an attempt to gain higher spatial resolution. Please excuse any off-putting terminology as I recently pivoted from functional protein dynamics recognition and prediction to BCI-EEG motor imagery classification. Feel free to share any thoughts, all advice is welcomed.
Thank you for your time,
Tyler J Grear
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EEG forecast the success of psychomotor test while reducing the level of wakefulness: an analysis of the results of the study / T. N. Shchukin, V. B. Dorokhov, A. N. Lebedev, E. V. Lutsenko // Polythematic network electronic scientific journal of Kuban state agrarian University (Scientific journal of Kubsau) [Electronic resource]. - Krasnodar: KubGAU, 2004. – №04(006). P. 290-306. - IDA [article ID]: 0060404022 – - Access mode: http://ej.kubagro.ru/2004/04/pdf/22.pdf, 1,062 cu. p. l.
EEG forecast the success of psychomotor test while reducing the level of wakefulness: a description of the experiment / T. N. Shchukin, V. B. Dorokhov, A. N. Lebedev, E. V. Lutsenko // Polythematic network electronic scientific journal of Kuban state agrarian University (Scientific journal of Kubsau) [Electronic resource]. - Krasnodar: KubGAU, 2004. – №04(006). P. 277-289. - IDA [article ID]: 0060404021 – - Access mode: http://ej.kubagro.ru/2004/04/pdf/21.pdf, 0.812 cu. p. l.
EEG forecast the success of psychomotor test while reducing the level of wakefulness: statement of the problem / T. N. Shchukin, V. B. Dorokhov, A. N. Lebedev, E. V. Lutsenko // Polythematic network electronic scientific journal of Kuban state agrarian University (Scientific journal of Kubsau) [Electronic resource]. - Krasnodar: KubGAU, 2004. – №04(006). P. 268-276. - IDA [article ID]: 0060404020 – - Access mode: http://ej.kubagro.ru/2004/04/pdf/20.pdf, 0.562 cu. p. l.
Thank you for the valuable insight Professor, I will make sure to read this work!
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