Novel EEG Quantification for Psychosis: Can We Analyze Vector Direction to Find A Pattern?

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Afkar Aulia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1634357/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Electroencephalography (EEG) had been a widely used diagnostic tool since its development in 1924. However, limitation exists, as it relies on visual recognition of spikes and quantification of brain waves. It might be possible to expand it's functionality by measuring the direction of electrical vectors, for example by comparing the value of coronal and sagittal voltage change during very short time period. This might be especially helpful for diseases with high inter-rater diagnosis bias, such as schizophrenia. We hoped that this method might expand research possibility and provided possible new method to reliably diagnose schizophrenia. Psychiatry Nuclear Medicine & Medical Imaging Computational Neuroscience Electroencephalography vector direction pattern analysis psychosis schizophrenia Background Electroencephalography (EEG) has long been known as a reliable method to assess seizure and brain electricity since its introduction in 1929 (Pantediallis, 2021). However, current usage of EEG limits the potential for assessment and diagnostic technology. The most common method used for EEG analysis is by finding spikes or by measuring alpha, beta, gamma, delta, or theta waves (Baang et al., 2022 ). Despite the usefulness in the analysis in various fields, EEG definitely has much more potential, as it actually has the capability to records waves occurring during very short duration, for example gamma wave, that has frequency up to 100 Hz (Braboszcz et al., 2017 ). This implies that EEG might actually be used to measure minute changes in every 0.01 second. On the other hand, schizophrenia and other psychotic disorders are mental health problems that are difficult to measure objectively. Diagnosis is based on subjective mental health examinations, which can cause high interrater bias (Jiang et al., 2022 ). There is a definite necessity to find methods to objectively measure schizophrenia or to monitor disease progression. EEG is based on change of potential measured by two nearby electrodes to measure very brief potential change. However, it’s interesting to note that, just like Electrocardiogram (ECG), EEG can measure vectors of electricity going away or towards every EEG node. These measurement of such vector is largely unused by today medicine. After all, it is difficult to measure and require complicated computer analysis with high RAM and specification. However, it’s interesting to note that big data is becoming easier to interpret nowadays, and thus, it might be the time to face EEG complete data more straightforwardly. For example, a program called Folding@home by Sony actually connects thousands of computers to analyze viral proteins simulation (Cubuk et al., 2021 ). It is no longer difficult to cut up large program task and have the computer analyze it for days by creating “save points” or just distribute the tasks to multiple computers. It’s also interesting to note that a method of measuring pattern of seemingly unrelatable sequence has been a hallmark of various medical improvement. One such example is genetic test. If we never measured lengthy and seemingly-unusable pattern of genetic exon sequence, the field would not improve as much as it does. It was possible that EEG analysis could benefit by applying method to measure repetitions as a possible novel approach. This method is one of the possible method to interpret big data from EEG record and change it to slightly less complicated number sequence based on the direction of electric vectors. Hopefully, by further understanding EEG records, we can probably understand how human brain works, and we can find better treatment method for psychosis. Method There are two phases of the study. As EEG analysis requires humongous amount of data, it’s inevitable that there are hundreds of hypothesis. We will later see that there are thousands of hypothesis to test. Therefore, to avoid “harking,” there is a need to divide the research into “hypothesis generating” phase and “hypothesis testing” phase in completely different subjects. We need to divide the subjects randomly for two phase of research. We need EEG recordings from 100 subjects diagnosed with schizophrenia and 100 subjects of healthy individual (or at least non-psychotic patient) for hypothesis generating, and then another EEG recordings of 100 patient with schizophrenia and 100 healthy individual for hypothesis testing. The subjects were randomized to the two phases from a pool of 200 schizophrenia patient EEG and 200 normal patient EEG. Hypothesis generating phase ( This is a stagnated research protocol. We have no physical nor mental resource to continue the idea, as author is having unwell mental condition. Thus, it is better to spread the idea to the community instead of holding it without much chance of continuation. However, if you are interested in the idea or have anything to query, please contact author email ( [email protected] ) ) Determine x-axis and y-axis. Sagittal axis in normal EEG reading, such as F7-T7, T7-P7, F4-C4, or others are considered y-axis. As normal EEG reading does not have x-axis (coronal axis), we need to generate them by making use of approximation using Avg axis For example, to generate Fp1 – Fp2, we need to substract potential in Fp2 – Avg with Fp1 –Avg. To generate F7 – F3, we can substract F3-Avg with F7-Avg If we compare potential change in every time-frame (every 0.04 s, or if we want to detail it further, 0.01 s) of adjacent x-axis with adjacent y-axis, we can get the degree of potential vector by using function f(x) = Arctan (dy/dx) Arctan (x) = reverse tangent, operation to find the degree of x from the known value of tan (x) dy = change of potential in y axis during very short time frame (for example, 0.01 s) dx = change of potential in x axis during very short time frame For example, if we want to know the direction of vector in plane F3-F7-Fp1 during the time between t=0.01s to t=0.02s, we need to observe potential change in axis F3-F7 and Fp1-F7 during such short time period. We can do that using program or simply using manual observation, for example by adding extra horizontal lines and extra vertical lines in EEG recording image. If during the observable time, Fp1-F7 has +7 mV change while F3-F7 has +1 mV change, we can find the direction by measuring arctan ((+7)/(+1)) = arctan 7 = 81.87 degree or 278.13 degree. As the formula results have two possible value, the positive value is used when dy is positive, and vice versa. The resulting pattern of degree is what will be analyzed. For this, we can use objective data of potential change in every milisecond, but if such data is not available, we can use image data of EEG recording, enlarge and analyze the change of potential visually for every 10 miliseconds. The 16 y-axis being matched with adjacent x-axis would result in 48 different planes to analyze. There are obviously multi-counted plane, for example, F7-F3-C3-T3 can be considered a square plane. However, to account for possible mistakes, I believe it’s better to analyze F3-F7-T3, F7-F3-C3, F3-C3-T3, and F7-T3-C3 separately. The resulting observable planes are: Fp1-F7-F3, F7-F3-Fp1, Fp1-F3-Fz, F3-Fz-Fp1, Fp2-Fp1-Fz, Fp1-Fp2-Fz, Fp2-Fz-F4, Fz-F4-Fp2, Fp2-F4-F8, F4-F8-Fp2, A1-T3-F7, F3-F7-T3, F7-T3-C3, T3-C3-F3, F7-F3-C3, FzF3C3 F3-C3-Cz, C3-Cz-Fz, F3-Fz-Cz, F4-Fz-Cz, Fz-Cz-C4, Cz-C4-F4, Fz-F4-C4, F8-F4-C4, F4-C4-T4, C4-T4-F8, F4-F8-T4, F8-T4-A2, A1-T3-T5, C3-T3-T5, T3-T5-P3, T5-P3-C3, T3-C3-P3, Cz-C3-P3, C3-P3-Pz, P3-Pz-Cz, C3-Cz-Pz, C4-Cz-Pz, Cz-Pz-P4, Pz-P4-C4, Cz-C4-P4, T4-C4-P4, C4-P4-T6, P4-T6-T4, C4-T4-T6, A2-T4-T6, P3-T5-O1, T5-P3-O1, Pz-P3-O1, P3-Pz-O1, Pz-O1-O2, O1-O2-Pz, P4-Pz-O2, Pz-P4-O2, T6-P4-O2, P4-T6-O2 Although most of them are not strictly perpendicular, the approximate vector direction should still hava values to observe. Obviously, the pattern gathered in step 2 would be extremely chaotic if we analyze the pattern as is, as there might be no exact repetition. We need algorythm to account for tolerable errors. We need to consider the possibility of lengthening of any segment, shortening of segment, and tolerable change of vector degree. It’s easier if we consider it as inserted or deleted segments. Find a pattern to test. Once we get the pattern which is suspected to be repeating (define it as “tested pattern”), we need to formulate all the possible tolerable alteration.Instead of the strict degree we get from the arctan function, we can regroup it into a new unit using degree range. For example, >0-18 degrees as 1 unit, >18-36 degree as 2 units, >36-54 degree as 3, ....., >162-180 degree as 10, >180-198 degree as -9,....., and >342-0 degree as 0 unit. That way, even if there is small degree difference, it can be considered the same pattern. Up to this point, we have analyzed and converted EEG sequence to number sequence. The method up to this part is already a novel approach. For example, we might find out that the first second of plane F3-F7-T3 is actually recoded as 100 consecutive values such as... 1 2 3 2 1 -1 -4 8 -8 7 10 8 -8 7 10 8 -8 7 10 3 2 1 -1 -4 8 -8 7 1 3 2 1 -1 -4 8 -8 7 1 ..... and so on. There might be multiple possibilities after this part of the method, as there is no exact repetition and there are various ways to interpret tolerable error. We will only discuss 1 possible method for this protocol draft. We need to set objective value of the tolerable alteration. I believe 20% difference from the original pattern can be considered tolerable such that it can be considered a repeating pattern. If we observe the pattern every 0.01 second, we can consider pattern of 1 second length as 100 segments. For alternate pattern of similar length (without insertion or deletion), we can calculate the “difference” with the formula: Difference from reference (%) = ((Number of segment with 1 unit difference)+ 2(Number of segments with 2 unit difference)+3(Number of segments with 3 unit difference)+...+10(number of segments with 10 unit difference))/(10 X number of segments) For segments with different length, or have been considered to have insertion or deletion, we need to create a new reference pattern. If we want to “insert” 10% extra length between two adjacent segment, it’d be easier to define the new inserted segment to have the mean degree of the two previously adjacent segment. The resulting reference pattern would then be considered to have 10% difference from the previous pattern. The new reference pattern can now be used to measure other patterns of the same length. Thus, Difference (%) = (percentage of inserted/deleted segment) + difference from new reference pattern (%) Note that it will be very confusing to use multiple reference patterns to check if other patterns are repetition of the original tested pattern. Instead, we use them to generate patterns that still fall within 20% difference of the original tested pattern. We would then simply match how many of the remaining patterns are tolerably a repeat of the tested pattern. This current phase might be exhaustive for humans, but current computer analysis might be able to do so. There are also methods to link multiple computers to cut the analysis. Sony’s “foldingathome” is one such program, but it might not necessarily be as complicated to cut the task into minitasks to be solved by 10-50 separate computers (we don’t need tens of thousands of computers for this). For example, for a tested pattern of 1s length (100 segments), we need to generate reference patterns of length 80 segments – 120 segments and then create “patterns of tolerable errors” to find out how many times the tested pattern is repeated for a certain period of time (for examples, the average number of repetition in 5 minutes). It becomes complicated since the pattern can be “inserted” or “deleted” in at most 20 different places (for 1s pattern) and each “insertion” can be of variable length with the total of 20 segments. A “tested pattern” can even get “inserted” and “deleted” at the same time in different areas to create new “reference pattern.” Now, from every “tested pattern,” we can get “patterns of tolerable errors” and how many times the pattern is repeated on average in 5 minutes. Now we can use mean comparison analysis such as t-test tocompare the number of repetition in schizophrenia patient with normal (non-psychotic patient). The pattern with significant result will be further tested for hypothesis testing. If we want to limit the pattern to pass to hypothesis testing phase, we might choose the pattern with lowest 10 p-value. The limitation is important since there might be tens of thousands of hypothesis which might mean that about 500 hypothesis are going to be mistakenly considered significant. It was obvious that there was a skipped explanation in step 5, which is an explanation of “which pattern is tested?”. However, since this is a new method, we have no idea what pattern is likely to be repeated and in which plane. The easiest method is to test them all. For example, begin by testing segments of 1s length in all subjects (segment 1-100, segment 2-101, segment 3-102, etc.) Since it’s possible that the subject is unique to the population, we need to check all patterns from the second subjects, third subjects, and so on, to find patterns that are relatively often repeated in all subjects. If we find less than 2% of all tested pattern is significant, we can test shorter pattern, for example 50 segments. If it still has too few significantly different patterns, we cut the length further to 25 segments. However, if there are too many patterns that are repeated in 1s length, for example, more than 15% of all patterns is significantly different between schizophrenia patiens and normal patients, we can consider longer pattern, for example 200 segments. The other possibility is to test random 1000 patterns, however, this approach is less favored, since there is significant loss of data.For simplicity, it’d better to compare only data of the same plane, but it might be an interesting prospect to compare data accros the plane or combination data from multiple planes (for example, segment 1 from plane 1, segment 2 from adjacent plane 2, and so on). Conclusion Electroencephalogram is a fascinating mechanism that measures brainwaves with potential to measure changes every 0.01 s. It was possible that EEG still has many hidden potential, for example by measuring pattern repetition of wave direction, which may reflect possible interactions of brain regions during very short time. It was possible that such measurement can be used for novel diagnostic approach for mental health problem such as psychosis or marker of disease progression. Declarations Competing interests: The authors declare no competing interests. References Baang, H.Y. et al. (2022) ‘The Utility of Quantitative EEG in Detecting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage’, Journal of Clinical Neurophysiology, 39(3), pp. 207–215. doi: 10.1097/WNP.0000000000000754 . Braboszcz, C. et al. (2017) ‘Increased Gamma Brainwave Amplitude Compared to Control in Three Different Meditation Traditions’, PLOS ONE . Edited by J. Najbauer, 12(1), p. e0170647. doi: 10.1371/journal.pone.0170647 . Cubuk, J. et al. (2021) ‘The SARS-CoV-2 nucleocapsid protein is dynamic, disordered, and phase separates with RNA’, Nature Communications , 12(1), p. 1936. doi: 10.1038/s41467-021-21953-3 . Jiang, Z. et al. (2022) ‘Utilizing computer vision for facial behavior analysis in schizophrenia studies: A systematic review’, PLOS ONE . Edited by F. Albu, 17(4), p. e0266828. doi: 10.1371/journal.pone.0266828 . Panteliadis, C.P. (2021) ‘Historical Overview of Electroencephalography: from Antiquity to the Beginning of the 21st Century’, Journal of Brain and Neurological Disorders, 3(1). doi: 10.5281/ZENODO.5359323 . Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1634357","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":104317657,"identity":"70d3bb01-16bb-48fa-b3b4-b0a676e52429","order_by":0,"name":"Afkar Aulia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIie2RsUoDQRCGJyzMNkuu3aBeXuEkELS5Z/E4uGsuVUCCBl0QNl18FX0DITBpbIUVC1OlshACBzYhE42SYq+wE9yPhRmY/Zh/WYBA4C8i6Ku2BbQWAPrwZ4Jw5lew2FUBImFF/ULho7mqvZlfiSxmqxGkmZVieeHGJwp0+XQH4xTa0q9owlnnEfLMCuy/VMTBdHXugHJAtfAHe52YjgHBCrCCn0rhAB84p39Ll+TNh4FrVmQ9rNbfyrpZSQiJt8xYUX0xsFulJNeyzcoxYXFqknmPleHBYKoVqjd02TRXTW+JCXvPZnR5dBvN71dVfRVHsly69zqNuxP/ll28vR5BJdsfUU2XfUh/oEAgEPi3bACbdU6tBvZQqQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-4001-9588","institution":"Universitas Gadjah Mada","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Afkar","middleName":"","lastName":"Aulia","suffix":""}],"badges":[],"createdAt":"2022-05-08 03:57:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1634357/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1634357/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21298561,"identity":"edb3673b-23c0-4e1d-bfac-7538c3e92819","added_by":"auto","created_at":"2022-05-10 17:37:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":209481,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1634357/v1/cbede4c9-ece4-485d-a18e-438cf77791d1.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eNovel EEG Quantification for Psychosis: Can We Analyze Vector Direction to Find A Pattern?\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eElectroencephalography (EEG) has long been known as a reliable method to assess seizure and brain electricity since its introduction in 1929 (Pantediallis, 2021). However, current usage of EEG limits the potential for assessment and diagnostic technology. The most common method used for EEG analysis is by finding spikes or by measuring alpha, beta, gamma, delta, or theta waves (Baang et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite the usefulness in the analysis in various fields, EEG definitely has much more potential, as it actually has the capability to records waves occurring during very short duration, for example gamma wave, that has frequency up to 100 Hz (Braboszcz et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This implies that EEG might actually be used to measure minute changes in every 0.01 second.\u003c/p\u003e \u003cp\u003eOn the other hand, schizophrenia and other psychotic disorders are mental health problems that are difficult to measure objectively. Diagnosis is based on subjective mental health examinations, which can cause high interrater bias (Jiang et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There is a definite necessity to find methods to objectively measure schizophrenia or to monitor disease progression.\u003c/p\u003e \u003cp\u003eEEG is based on change of potential measured by two nearby electrodes to measure very brief potential change. However, it\u0026rsquo;s interesting to note that, just like Electrocardiogram (ECG), EEG can measure vectors of electricity going away or towards every EEG node. These measurement of such vector is largely unused by today medicine. After all, it is difficult to measure and require complicated computer analysis with high RAM and specification. However, it\u0026rsquo;s interesting to note that big data is becoming easier to interpret nowadays, and thus, it might be the time to face EEG complete data more straightforwardly. For example, a program called Folding@home by Sony actually connects thousands of computers to analyze viral proteins simulation (Cubuk et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is no longer difficult to cut up large program task and have the computer analyze it for days by creating \u0026ldquo;save points\u0026rdquo; or just distribute the tasks to multiple computers.\u003c/p\u003e \u003cp\u003eIt\u0026rsquo;s also interesting to note that a method of measuring pattern of seemingly unrelatable sequence has been a hallmark of various medical improvement. One such example is genetic test. If we never measured lengthy and seemingly-unusable pattern of genetic exon sequence, the field would not improve as much as it does. It was possible that EEG analysis could benefit by applying method to measure repetitions as a possible novel approach.\u003c/p\u003e \u003cp\u003eThis method is one of the possible method to interpret big data from EEG record and change it to slightly less complicated number sequence based on the direction of electric vectors. Hopefully, by further understanding EEG records, we can probably understand how human brain works, and we can find better treatment method for psychosis.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThere are two phases of the study. As EEG analysis requires humongous amount of data, it\u0026rsquo;s inevitable that there are hundreds of hypothesis. We will later see that there are thousands of hypothesis to test. Therefore, to avoid \u0026ldquo;harking,\u0026rdquo; there is a need to divide the research into \u0026ldquo;hypothesis generating\u0026rdquo; phase and \u0026ldquo;hypothesis testing\u0026rdquo; phase in completely different subjects.\u003c/p\u003e \u003cp\u003eWe need to divide the subjects randomly for two phase of research. We need EEG recordings from 100 subjects diagnosed with schizophrenia and 100 subjects of healthy individual (or at least non-psychotic patient) for hypothesis generating, and then another EEG recordings of 100 patient with schizophrenia and 100 healthy individual for hypothesis testing. The subjects were randomized to the two phases from a pool of 200 schizophrenia patient EEG and 200 normal patient EEG.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis generating phase\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cem\u003eThis is a stagnated research protocol. We have no physical nor mental resource to continue the idea, as author is having unwell mental condition. Thus, it is better to spread the idea to the community instead of holding it without much chance of continuation. However, if you are interested in the idea or have anything to query, please contact author email ([email protected])\u003c/em\u003e)\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eDetermine x-axis and y-axis. Sagittal axis in normal EEG reading, such as F7-T7, T7-P7, F4-C4, or others are considered y-axis. As normal EEG reading does not have x-axis (coronal axis), we need to generate them by making use of approximation using Avg axis\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor example, to generate Fp1 \u0026ndash; Fp2, we need to substract potential in Fp2 \u0026ndash; Avg with Fp1 \u0026ndash;Avg. To generate F7 \u0026ndash; F3, we can substract F3-Avg with F7-Avg\u003c/p\u003e\n\u003col start=\"2\" type=\"1\"\u003e\n \u003cli\u003eIf we compare potential change in every time-frame (every 0.04 s, or if we want to detail it further, 0.01 s) of adjacent x-axis with adjacent y-axis, we can get the degree of potential vector by using function\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ef(x) = Arctan (dy/dx)\u003c/p\u003e\n\u003cp\u003eArctan (x) = reverse tangent, operation to find the degree of x from the known value of tan (x)\u003c/p\u003e\n\u003cp\u003edy = change of potential in y axis during very short time frame (for example, 0.01 s)\u003c/p\u003e\n\u003cp\u003edx = change of potential in x axis during very short time frame\u003c/p\u003e\n\u003cp\u003eFor example, if we want to know the direction of vector in plane F3-F7-Fp1 during the time between t=0.01s to t=0.02s, we need to observe potential change in axis F3-F7 and Fp1-F7 during such short time period. We can do that using program or simply using manual observation, for example by adding extra horizontal lines and extra vertical lines in EEG recording image. If during the observable time, Fp1-F7 has +7 mV change while F3-F7 has +1 mV change, we can find the direction by measuring arctan ((+7)/(+1)) = arctan 7 = 81.87 degree or 278.13 degree.\u003c/p\u003e\n\u003cp\u003eAs the formula results have two possible value, the positive value is used when dy is positive, and vice versa. The resulting pattern of degree is what will be analyzed. For this, we can use objective data of potential change in every milisecond, but if such data is not available, we can use image data of EEG recording, enlarge and analyze the change of potential visually for every 10 miliseconds.\u003c/p\u003e\n\u003col start=\"3\" type=\"1\"\u003e\n \u003cli\u003eThe 16 y-axis being matched with adjacent x-axis would result in 48 different planes to analyze. There are obviously multi-counted plane, for example, F7-F3-C3-T3 can be considered a square plane. However, to account for possible mistakes, I believe it\u0026rsquo;s better to analyze F3-F7-T3, F7-F3-C3, F3-C3-T3, and F7-T3-C3 separately.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe resulting observable planes are:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"166\" src=\"data:image/png;base64,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\" alt=\"electrode\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003cstrong\u003eFp1-F7-F3, F7-F3-Fp1, Fp1-F3-Fz, F3-Fz-Fp1, \u0026nbsp;Fp2-Fp1-Fz,\u0026nbsp;\u003c/strong\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFp1-Fp2-Fz, Fp2-Fz-F4, Fz-F4-Fp2, Fp2-F4-F8, F4-F8-Fp2,\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA1-T3-F7, F3-F7-T3, F7-T3-C3, T3-C3-F3, F7-F3-C3, FzF3C3\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF3-C3-Cz, C3-Cz-Fz, F3-Fz-Cz, F4-Fz-Cz, Fz-Cz-C4, Cz-C4-F4,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFz-F4-C4, F8-F4-C4, F4-C4-T4, C4-T4-F8, F4-F8-T4, F8-T4-A2,\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA1-T3-T5, C3-T3-T5, T3-T5-P3, T5-P3-C3, T3-C3-P3, Cz-C3-P3,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC3-P3-Pz, P3-Pz-Cz, C3-Cz-Pz, C4-Cz-Pz, Cz-Pz-P4, Pz-P4-C4,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCz-C4-P4, T4-C4-P4, C4-P4-T6, P4-T6-T4, C4-T4-T6, A2-T4-T6,\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eP3-T5-O1, T5-P3-O1, Pz-P3-O1, P3-Pz-O1, Pz-O1-O2, O1-O2-Pz, P4-Pz-O2,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePz-P4-O2, T6-P4-O2, P4-T6-O2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough most of them are not strictly perpendicular, the approximate vector direction should still hava values to observe.\u003c/p\u003e\n\u003col start=\"4\" type=\"1\"\u003e\n \u003cli\u003eObviously, the pattern gathered in step 2 would be extremely chaotic if we analyze the pattern as is, as there might be no exact repetition. We need algorythm to account for tolerable errors. We need to consider the possibility of lengthening of any segment, shortening of segment, and tolerable change of vector degree. It\u0026rsquo;s easier if we consider it as inserted or deleted segments.\u003c/li\u003e\n \u003cli\u003eFind a pattern to test. Once we get the pattern which is suspected to be repeating (define it as \u0026ldquo;tested pattern\u0026rdquo;), we need to formulate all the possible tolerable alteration.Instead of the strict degree we get from the arctan function, we can regroup it into a new unit using degree range. For example, \u0026gt;0-18 degrees as 1 unit, \u0026gt;18-36 degree as 2 units, \u0026gt;36-54 degree as 3, ....., \u0026gt;162-180 degree as 10, \u0026gt;180-198 degree as -9,....., and \u0026gt;342-0 degree as 0 unit. That way, even if there is small degree difference, it can be considered the same pattern.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eUp to this point, we have analyzed and converted EEG sequence to number sequence. The method up to this part is already a novel approach. For example, we might find out that the first second of plane F3-F7-T3 is actually recoded as 100 consecutive values such as...\u003c/p\u003e\n\u003cp\u003e1 2 3 2 1 -1 -4 8 -8 7 10 8 -8 7 10 8 -8 7 10 3 2 1 -1 -4 8 -8 7 1 3 2 1 -1 -4 8 -8 7 1 ..... and so on. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere might be multiple possibilities after this part of the method, as there is no exact repetition and there are various ways to interpret tolerable error. We will only discuss 1 possible method for this protocol draft.\u003c/p\u003e\n\u003col start=\"6\" type=\"1\"\u003e\n \u003cli\u003eWe need to set objective value of the tolerable alteration. I believe 20% difference from the original pattern can be considered tolerable such that it can be considered a repeating pattern.\u003c/li\u003e\n \u003cli\u003eIf we observe the pattern every 0.01 second, we can consider pattern of 1 second length as 100 segments. For alternate pattern of similar length (without insertion or deletion), we can calculate the \u0026ldquo;difference\u0026rdquo; with the formula:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eDifference from reference (%) =\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e((Number of segment with 1 unit difference)+ 2(Number of segments with 2 unit difference)+3(Number of segments with 3 unit difference)+...+10(number of segments with 10 unit difference))/(10 X number of segments)\u003c/p\u003e\n\u003col start=\"8\" type=\"1\"\u003e\n \u003cli\u003eFor segments with different length, or have been considered to have insertion or deletion, we need to create a new reference pattern. If we want to \u0026ldquo;insert\u0026rdquo; 10% extra length between two adjacent segment, it\u0026rsquo;d be easier to define the new inserted segment to have the mean degree of the two previously adjacent segment. The resulting reference pattern would then be considered to have 10% difference from the previous pattern. The new reference pattern can now be used to measure other patterns of the same length. Thus,\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eDifference (%) = (percentage of inserted/deleted segment) + difference from new reference pattern (%)\u003c/p\u003e\n\u003cp\u003eNote that it will be very confusing to use multiple reference patterns to check if other patterns are repetition of the original tested pattern. Instead, we use them to generate patterns that still fall within 20% difference of the original tested pattern. We would then simply match how many of the remaining patterns are tolerably a repeat of the tested pattern. This current phase might be exhaustive for humans, but current computer analysis might be able to do so. There are also methods to link multiple computers to cut the analysis. Sony\u0026rsquo;s \u0026ldquo;foldingathome\u0026rdquo; is one such program, but it might not necessarily be as complicated to cut the task into minitasks to be solved by 10-50 separate computers (we don\u0026rsquo;t need tens of thousands of computers for this).\u003c/p\u003e\n\u003cp\u003eFor example, for a tested pattern of 1s length (100 segments), we need to generate reference patterns of length 80 segments \u0026ndash; 120 segments and then create \u0026ldquo;patterns of tolerable errors\u0026rdquo; to find out how many times the tested pattern is repeated for a certain period of time (for examples, the average number of repetition in 5 minutes). It becomes complicated since the pattern can be \u0026ldquo;inserted\u0026rdquo; or \u0026ldquo;deleted\u0026rdquo; in at most 20 different places (for 1s pattern) and each \u0026ldquo;insertion\u0026rdquo; can be of variable length with the total of 20 segments. A \u0026ldquo;tested pattern\u0026rdquo; can even get \u0026ldquo;inserted\u0026rdquo; and \u0026ldquo;deleted\u0026rdquo; at the same time in different areas to create new \u0026ldquo;reference pattern.\u0026rdquo;\u003c/p\u003e\n\u003col start=\"9\" type=\"1\"\u003e\n \u003cli\u003eNow, from every \u0026ldquo;tested pattern,\u0026rdquo; we can get \u0026ldquo;patterns of tolerable errors\u0026rdquo; and how many times the pattern is repeated on average in 5 minutes. Now we can use mean comparison analysis such as t-test tocompare the number of repetition in schizophrenia patient with normal (non-psychotic patient). The pattern with significant result will be further tested for hypothesis testing. If we want to limit the pattern to pass to hypothesis testing phase, we might choose the pattern with lowest 10 p-value. The limitation is important since there might be tens of thousands of hypothesis which might mean that about 500 hypothesis are going to be mistakenly considered significant.\u003c/li\u003e\n \u003cli\u003eIt was obvious that there was a skipped explanation in step 5, which is an explanation of \u0026ldquo;which pattern is tested?\u0026rdquo;. However, since this is a new method, we have no idea what pattern is likely to be repeated and in which plane. The easiest method is to test them all. For example, begin by testing segments of 1s length in all subjects (segment 1-100, segment 2-101, segment 3-102, etc.) Since it\u0026rsquo;s possible that the subject is unique to the population, we need to check all patterns from the second subjects, third subjects, and so on, to find patterns that are relatively often repeated in all subjects. If we find less than 2% of all tested pattern is significant, we can test shorter pattern, for example 50 segments. If it still has too few significantly different patterns, we cut the length further to 25 segments. However, if there are too many patterns that are repeated in 1s length, for example, more than 15% of all patterns is significantly different between schizophrenia patiens and normal patients, we can consider longer pattern, for example 200 segments. The other possibility is to test random 1000 patterns, however, this approach is less favored, since there is significant loss of data.For simplicity, it\u0026rsquo;d better to compare only data of the same plane, but it might be an interesting prospect to compare data accros the plane or combination data from multiple planes (for example, segment 1 from plane 1, segment 2 from adjacent plane 2, and so on).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Conclusion","content":"\u003cp\u003eElectroencephalogram is a fascinating mechanism that measures brainwaves with potential to measure changes every 0.01 s. It was possible that EEG still has many hidden potential, for example by measuring pattern repetition of wave direction, which may reflect possible interactions of brain regions during very short time. It was possible that such measurement can be used for novel diagnostic approach for mental health problem such as psychosis or marker of disease progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCompeting interests: The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaang, H.Y. \u003cem\u003eet al.\u003c/em\u003e (2022) \u0026lsquo;The Utility of Quantitative EEG in Detecting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage\u0026rsquo;, Journal of Clinical Neurophysiology, 39(3), pp.\u0026nbsp;207\u0026ndash;215. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/WNP.0000000000000754\u003c/span\u003e\u003cspan address=\"10.1097/WNP.0000000000000754\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBraboszcz, C. \u003cem\u003eet al.\u003c/em\u003e (2017) \u0026lsquo;Increased Gamma Brainwave Amplitude Compared to Control in Three Different Meditation Traditions\u0026rsquo;, \u003cem\u003ePLOS ONE\u003c/em\u003e. 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(2021) \u0026lsquo;Historical Overview of Electroencephalography: from Antiquity to the Beginning of the 21st Century\u0026rsquo;, Journal of Brain and Neurological Disorders, 3(1). doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/ZENODO.5359323\u003c/span\u003e\u003cspan address=\"10.5281/ZENODO.5359323\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Electroencephalography, vector direction, pattern analysis, psychosis, schizophrenia","lastPublishedDoi":"10.21203/rs.3.rs-1634357/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1634357/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElectroencephalography (EEG) had been a widely used diagnostic tool since its development in 1924. However, limitation exists, as it relies on visual recognition of spikes and quantification of brain waves. It might be possible to expand it's functionality by measuring the direction of electrical vectors, for example by comparing the value of coronal and sagittal voltage change during very short time period. This might be especially helpful for diseases with high inter-rater diagnosis bias, such as schizophrenia. We hoped that this method might expand research possibility and provided possible new method to reliably diagnose schizophrenia.\u003c/p\u003e","manuscriptTitle":"Novel EEG Quantification for Psychosis: Can We Analyze Vector Direction to Find A Pattern?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-10 17:37:21","doi":"10.21203/rs.3.rs-1634357/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ea9a4636-c003-453d-a1e4-43bb5245a605","owner":[],"postedDate":"May 10th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":12411914,"name":"Psychiatry"},{"id":12411915,"name":"Nuclear Medicine \u0026 Medical Imaging"},{"id":12411916,"name":"Computational Neuroscience"}],"tags":[],"updatedAt":"2022-05-10T17:37:21+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-10 17:37:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1634357","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1634357","identity":"rs-1634357","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}