Category: EEG

Journal page

https://onlinelibrary.wiley.com/doi/epdf/10.1111/ejn.15131

AudioMaze is Swartz Center’s main Mobile Brain-Body Imaging (MoBI) projects that was multi-million-dollar funded, and the dedicated lab has been in ‘the downstairs’ for more than a decade. Basically, the idea is that a blindfolded subject walks through the digitally defined mazes which has in programmable walls that returns audio feedback when touched, hence AudioMaze. Because blindfolded subjects need to walk very slowly, and the spatial probing is limited to stretching the right arm, subject’s behaviors becomes very slow, stereotypical, and repeated, all of which is suitable for event-related brain potential analysis.

The highlight of the finding is that the occipital regions, which is typically associated with visual processes, showed modulated activities although the subjects were blindfolded. My colleagues in Berlin has already published dozens of navigation related studies and suggested that retrosplenial cortex, which is right under the posterior cingulate cortex, is involved in this process. Generally speaking, that could be the truth of functional brain mapping. However, when it comes to detecting retrosplenial cortex with scalp EEG recording, it sounds too ambitious for me. I found in literature that lingual gyrus is a posterior part of parahippocampal place area (PPA), which seems to fit as the explanation of the result I got from the study. So I put more weights on the lingual gyrus in the interpretation.

As an experimental approach in EEG analysis, I used group-level source information flow toolbox, groupSIFT, to calculate effective connectivity across probabilistic independent component densities resolved in the brain regions at the group-level analysis. Not surprisingly, I found time-evolving effective connectivity networks centered at the occipital and inferior parietal regions.

This paper has 25 pages, 12000 words, and 14 figures (plus 3 additional figures in the Supplement). I told grad students and post-docs that they should not write a paper like this because it risks their careers. The experimental paradigm is new, the analysis is new, no psychologically clever top-down question is available, etc. In a sense, it was good that I took this job because I was old enough.

This short technical paper was originally written to an opinion paper to ZapLine paper.

https://www.sciencedirect.com/science/article/pii/S1053811919309474

Therefore, the title makes sense only in that context. However, because the submission was rejected, now it does not make much sense. One of the coauthors pointed it out to me only after publishing the paper.

Journal page

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873913/

The point of this paper is that even though ZapLine is an amazing line-noise reduction solution by itself, as the original author called it ‘quasi-perfect’ in the highlight points, there is actually an easy way to improve the performance. That is, to apply CleanLine after ZapLine. Why can I predict it? It is because ZapLine is a spatial filter method, while CleanLine is a non-stationary method in the time domain.

One of the reviewers said this is not worth a paper because there is no novel finding or development. Yes, it has a good point. But at the same time I believe that sharing this idea supported by an insight and evidence is also an important part of scientific communication.

By the way, the original author of ZapLine, Alain de Cheveigné kindly helped me to write this paper, although I was teasing his phrase ‘quasi perfect’ in the manuscript. I deeply appreciate his generousity!

The first author Nattapong visited me at the Swartz Center in UCSD a few years ago. At that time, I was fascinated by re-inventing (without knowing, of course) the idea of comodugram/comodulogram, which is a visualization of cross-frequency power-power coupling (also called spectral covariance). I suggested the publication of this idea to Nattapong, and he agreed. Then the idea was published.

Journal page

https://www.mdpi.com/1424-8220/20/24/7040/htm

EEGLAB plugin Wiki page

https://github.com/sccn/PowPowCAT

Sometimes you see two peaks in power spectral density (PSD) of EEG signals. But have you seen four peaks!? The owner of this brain is a grad student in our lab.

This comodulogram has been probably most frequently used in Buzsáki lab in the field that is closest to mine. In fact, Nattapong and I wrote them to ask about it. They were so kind, György pointed us to several of his colleagues and they told us several interesting things about this method.

Initially, I did not even know the name of this visualization. I asked it to a couple of smart people, but no one knew it. Later, Nattapong and I learned that it is called ‘comodugram’. But I did not like the name. The other, slightly less common name ‘comodulogram’ sounds better. But I like the fully descriptive version of the name, Power-Power Coupling Analysis Tool (PowPowCAT). Actually, I would be motivated to publish any computational tool that has the name as nice as PowPowCAT.

This is a part of a study on chronic tic disorder conducted by Sandra Loo at UCLA. The idea is to investigate the effect of blink suppression in typically developed children. She included a unique idea of instructing the children to use ‘urgeometer’, which is just a customized joystick, to self-report the amount of urge in real time. Basically, I could find a pretty straightforward prefrontal involvement during voluntary blink suppression.

The link to the full paper (open access) is here.

https://academic.oup.com/cercorcomms/article/1/1/tgaa046/5881803

There was actually another intention in this paper, which is more related to EEG methodology. Think about the reason why this kind of study has not been done so far–it is because eye blink is a catastrophic artifact for a standard EEG analysis. I thought it is my responsibility to show minimum level of validation to justify my blink-related EEG analysis. This endeavor is, not surprisingly, reported in the Supplement.

https://oup.silverchair-cdn.com/oup/backfile/Content_public/Journal/cercorcomms/1/1/10.1093_texcom_tgaa046/1/supplementary_material_revision1_tgaa046.docx?Expires=1616171516&Signature=syI9xbw3xWlhjqmNZVhLRacw2pwi8CjXFQhWyo8fiertNMOdIFh80IyS6UZTs3QCtANgEFTfu89sFJpi1lexyNuLX-UGdJzEKWWbAqp0DwW3oEHh0EapvQYalfj33BnqZz5GglE0LBMde-L7VU~CZl63KqWd4Ilg1YY5rq7kV3p735YUGShpnraU8KSmW6qzsbzUpVhrJYoQnuyT-uBjS~-dzql476zQI5xobte3WiOkciUPPsQBYv9YdmJ8KeuKQZ~vBTut43pqjgq3hcj4OT4Se2gEoN2x63ayw3VwiFjUwONiM7f2u2fBxg~M7MYXY56lx6xMZgHeMJKJw-KCeQ__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA

For example, this is the before-after comparison. Apparently, the amplitude scale is 40 times different in this comparison! According to what I reported, average 98.4% of signal variance was removed from scalp-electrode signals. I thought this number is insane, so I wanted to check it.

How can I show that bathwater was thrown out, not the baby? The right way to do it would require a well-designed simulation study, which I could not afford. So I attempted to show the best circumstantial evidence that is available as a post-hoc validation using an ok-level-designed simulation.

I added a small ground-truth signal (1% of unprocessed signal standard deviation) in the scalp signal, then applied the data cleaning method, artifact subspace reconstruction (ASR) + independent component analysis (ICA) to see if the ground-truth signal survives. This process was repeated for the different levels of SNR which was progressively lowered. The result was like this.

I found that the IC-scalp topography became suddenly worse when SNR is lowered to less than 30%. So I concluded that the small signal (1% of unprocessed signal standard deviation) can easily survive the current ASR + ICA methold. Moreover, it has further SNR margin of 7dB.

This ok-level-design was whipped up for a quick proof of concept, but it seems to deserve further development into a full method paper. However, I have so many of this kind of ideas that I can’t publish all of them. If there are grad students or post-docs interested in this idea, I would be happy to collaborate.

I have been translating the second edition of ‘Electric Field of the Brain’ by Nunez and Srinivasan (2006) into Japanese since November 2018. I have not found a publisher in Japan but it does not matter. It takes 3-4 month for me to translate one chapter which is about 50 pages. There are 11 chapters in 530 pages, 12 appendices in 74 pages, and 6 pages of index list, total of 610 pages. I skipped Chapter 3 to finish an abridged version of the translation first, which was suggested by one of the publishers I talked to. The senior author Dr. Paul Nunez suggested the Chapter 3 may be omitted. I am reading while translating, so probably I am the slowest reader of this book ever. But totally enjoy the process.

I found the 1st edition of this book in SCCN’s library in the early 2010’s. I opened random pages to scan. One of the figures attracted my attention.

On the left hand side, there are the brain, an occipital superficial dipole sheet, the resultant electric field, scalp electrodes, and a voltage meter circuit. So far so good. On the right hand space, infinity, a foot, and a STOOL. This is a graffiti, but I like the fact that the author made the authoritative Oxford University Press publish it. The figure rocks.

Another my favorite part of the book is the quote from his mentor Reginald Bickford, taken from from the Foreword to the 1st edition: ” … the authors have fallen so naturally into the lingo of the specialty while feeling free to slaughter many sacred cows that clutter the field.” This Foreword rocks too!