 Test the chat It's working. Okay, I can't hear myself still. Well, that's because I'm using the wrong headphones Now it should be working maybe It's the what five six five ten six second delay. That is pretty annoying, but we can manage Can live with that? Live live? Okay, still can't hear myself. That's because the Tof Headphones are not connected Sounds like this live stream is a good start Fully it picks up. I have to keep talking just so I can test my own sound What the thumbnail for this live streams it will be wrong Let's refresh the page again. No, it's still wrong doing live development. Just in case anyone Wants to join in. I have to. It's the post about it first Start in a sec And this is a good place to be if you are sick of meetings I'm not sure why they, why they all the thumbnail, maybe a different thumbnail for this video. Anyway, it doesn't matter. I can manage So if you've seen the last couple of videos, we are doing an eG2 music conversion Before that couple of things to try out talk about So we updated Visual Studio Code with a November release version 1.85 And the interesting thing is I'm gonna try The new GitHub co-pilot chat Okay, let's seem to Remove that entirely or something Okay, let's change this interface Okay, can start a new session Material when we type add Symbol should, should give us another option there It doesn't Right, it's actually here Why? Okay, so workspace So if we ask where is eG2 music version Should be able to search the whole All the files, so it's searching something Doing something and yeah, still get this array buffer allocation fail So they have videos showing how it's working. Well, I don't know why It's not working for me I'm on Linux Ubuntu. So that's obviously The Microsoft's reason for it not working That's okay. Yeah, they keep changing it all the time. So it's kind of almost pointless reading those instructions because It's obviously a work in progress Close that Start a new chat Just another quick thing Not related to what we're doing Yes, why would talk about it? GitHub co-pilot can Explain this code Let it run for a while While it's doing that and there's two papers on there EG portable systems for This is a walk response potential research Event related brain potential No, why? Yeah, eventually that that makes sense So it's a display. It's an eG response to a certain stimulus and could be vision sound anything really But this paper We would not have time to read it all especially because they keep coming up. There's another paper similar Normally just when looking papers at some of the images Yeah, not a great image there. That's the study study paradigm. I assume Not an easy image to follow And those are get those should have been in different colors. Come on But yeah, even without the different colors you can supposedly see the difference in response When stimulus is present And this is amplitude Microvolts up to the eG so you can see it's bugger all in this case like five a microvolt a change No, plus minus six seven microvolts You can also see interestingly that this signal is not detrended. There's that some DC In it Especially in this third example, you know what the difference between the three examples Anyway, we would not have time to read this papers so we Want to use chpd for it. So if I pop the link in interestingly enough The NIH a website, I Think just doesn't have gives permission or GPT-4 to crawl and crawl the site I Think that's the reason why I'm getting this A message from GPT-4. This is the other paper I'm able to access and in the beginning of the article you have this DOI Link which goes to the original paper. It's in From TES in neuroscience. So what happens if we put this link in Trying to access that website with Bing Actually able to think just pick up the abstract of the article Read that weekly also Validation of a low-cost portable EEG system for ERP research from frontiers in neuroscience discusses the validation of the muse EEG system for a ventilated potential ERP research It demonstrates the muse a portable and affordable EEG system You effectively capture and analyze ERP components like N 200 P 300 and reward positivity typically used in cognitive and perceptual studies The research involved comparing data from the MUSC system with that from a standard EEG setup showing MUSC's potential for ERP research in various contexts Would you try reading the whole article? I'm mainly after any data published in association to this article Yep, let's scrolling that page again Skim in this Responses it keeps referring clear. Can you read the whole article? Yep, what are the key findings? Yep, it's still giving me stuff from the abstract The muse EEG system effectively captures ERP components like N 200 P 300 and reward positivity It's comparable to standard EEG systems in capturing these components MUSC's portability and affordability make it suitable for various research contexts including cognitive and perceptual studies Have you actually read the whole article or are you giving me information from the abstract and long? My main question is how does the muse EEG device quality signal compared to our EEG recording paradigms Yep, that says I don't have a Access to full text before I'm pretty sure it said I've read the article Yeah, so this day Bit confusing. I was pretty sure it's just giving me stuff from the Rome the abstract Clear now question should we do this question So this article is Creative commons open source Being can't download PDF. We just work on it to register side. No PDF was just downloaded Have the PDF now GPT for Can you read the whole article? Can you read the whole article and summarize? I'm mainly after Information regards the data set used for this article. Is it publicly available? Not sure if you allowed to do it or not You meet the link to the article and why You have it or a article choosing news validation of a low-cost portable EEG system for ERP research primarily examines the effectiveness of the muse EEG system for recording event related potentials out The study involves 60 participants and compared data from the MUSC system with a standard EEG setup Key findings include the MUSC systems ability to capture ERP components like N200 P300 and reward positivity comparable to a standard EEG It emphasizes MUSC's potential for cognitive and perceptual studies due to affordability and portability For detailed data and methodology, please refer to the full article You read the entire article. Did you read the whole article is That data set used in this study publicly available Yep, it's reading it again. So obviously this would be different from Uploading the actual text into GPT Just trying to work with the PDF The article doesn't specify the data set using publicly available Can still train How was the data collected what they multiple devices compared? Yep in a study EEG data were collected from 60 participants using both the muse EEG system and the standard EEG system to compare their performance Multiple devices were indeed compared the portable muse system and a laboratory grade EEG system The comparison focused on the accuracy and reliability of the MUSC system in capturing specific ERP Assessing its suitability for cognitive and perceptual research For more details on the data collection methods and device comparison you can refer to the full article here Like you to read the whole article and not refer to the link anymore Could you explain the figures? particularly figures 2 to 4 doesn't seem to have I'm pretty sure I didn't read the whole article. I don't know if it's reading it now as we go It keep referring to the article compares the muse EEG system with a standard EEG setup across figures 2 to 4 These figures present conditional waveforms and difference waveforms for different tasks and analysis methods showcasing the performance of the MUSC system compared to the standard setup They visually represent the reliability and quality of the MUSC system in capturing ERP components The comparison is made across different conditions and tasks indicating the MUSC systems effectiveness in a variety of scenarios For a detailed view and understanding of these figures, please refer to the full article here again without referencing the article in a link online link could you explain the details of figure number two Figure two in the article presents the ERP waveforms recorded from the MUSC system and the standard EEG system It compares these waveforms for different components and conditions illustrating the similarities and differences in data captured by both systems This figure is crucial for understanding the efficacy of the MUSC system in accurately recording ERP components compared to the traditional EEG setup It's a really bad figure to understand should be labels Conditional waveforms standard analysis electrode PZ The top is the electrode PZ middle reduced the mouse to put electrode TP 9 TP 10 and bottom is the MUSC Yeah, obviously this was cherry picked as well Where can I find the raw data for this study? The raw data for the study presented in the article is not specified as being publicly available within the article itself Typically such data sets may be provided in supplementary materials or in a data repository mentioned in the article If not explicitly stated researchers often contact the study authors directly to inquire about data availability for further research or analysis Should I read papers that do not publish the raw data? Whether to read papers without published raw data depends on your research needs and standards Papers without raw data can still offer valuable insights theories and methodologies However, if reproducibility and data analysis are crucial for your work access to raw data might be a determining factor The decision ultimately depends on your research objectives and the importance of data transparency in your field of study Well, it's also that the figures in these papers are not great. I would like to reproduce them in a different way So they actually made clearer Can you explain figure five? Figure five in the article presents the mean amplitudes of the N200, P300 and reward positivity ERP components across three different analyses standard reduced and MEWS The figure includes 95 confidence intervals free to component This visual comparison helps in understanding the performance of the MUSC system in detecting these ERP components relative to the standard EEG system So while the reduced and MEWS conditions are similar There seem to be a significant difference between the standard EEG recording both in N200 and P300 Yes, figure five indicates significant differences between the standard EEG and the reduced MUSC conditions in capturing N200 and P300 components This highlights the variations in data quality and accuracy between the systems particularly in detecting specific ERP components Such differences are important for evaluating the MUSC system's suitability for various research applications About reward positivity study In the reward positivity aspect of the study figure five shows a comparison of the MUSC and standard EEG systems The figure likely indicates the differences in capturing the reward positivity component Which is crucial for understanding the effectiveness and limitations of the MUSC system in this specific aspect of ERP research This data helps in assessing the MUSC system's overall utility for various ERP components including reward positivity Can you extract the data from the figure? Yep Anyway, I have no idea why So this is normal N200 Extracting specific data from a figure in a PDF document is beyond my capabilities However, you can manually analyze the figure by observing the graph data points and comparing the mean amplitudes and confidence intervals for each condition standard reduced MUSC This analysis can provide insights into the performance differences of the MUSC system in capturing the reward positivity component This is the figure. Can you extract data from it? Yep Yep Guess it's actually using a PLO library It's using OCR to extract text It didn't do a great job extracting the text I don't know how does it know it's successful or not. I mean it's extracted something The OCR process did not successfully extract the detailed data from the figure To accurately extract the data from the bar graphs for N200, P300, and reward positivity We would typically use image processing techniques to analyze the pixel values and convert them into numerical data However, this process can be complex and may not always yield precise results due to variations in image quality and graph formatting To proceed, I can attempt to manually estimate the values from the bar graphs based on their relative positions and the scale provided in the figure Would you like me to do that? Let's do that. So now to the numpy ring image Converting to grayscale thresholding In vet Find contours Yeah, there will be problem with those contours The background bit over there. Which is anyway, it's not a great graph Filter contours It's not only find It's in the cream The contour detection approach did not yield the desired outcome for data extraction Instead we can employ a more direct method by estimating the mean amplitude values from the bar plots based on the y-axis scale I will proceed with this manual estimation approach to approximate the values for each component N200, P300, reward positivity from the bar plots provided Let's analyze the image to estimate these values Right, so now it's doing another Estimate Showing the figure So I estimated the values for N200 Standard mean minus 5 No more Yeah, I think it's really struggling with this image Because the same values Mean approximately minus 5 micromic SU Min Massachusetts News Standard mean approximately 5 micromic Reduced mean approximately five sea range approximately 0 to 10 microvolts the values are wrong yep i don't know why i don't know why this figure is like that it seems like to begin with a very bad a bad figure but i wonder if there is a reasoning behind it was it meant to confuse OCR and image detection it definitely confuses the human but the contour detection was successful in identifying the bar shapes from the figure the fact that at data extraction we need to measure the heights of the bars against the y-axis scale this is typically done by calculating the pixel distance between the top and bottom of each bar then converting these pixel measurements to corresponding voltage values using the scale of the y-axis however the current setup of the code detects all contours not just bars and doesn't provide the data extraction functionality to extract the data we would need a more complex analysis which includes isolating each bar measuring it and mapping it to y-axis scale that's similar to the original code what is it doing now right so it extracted some numbers why they're so large they obviously the absolute value is wrong i wonder if it's still correct in terms of the ratio between them doesn't look like yeah no it's wrong there was an error in the code you didn't correct indexing the image is actual size and y-axis pixel positions need to be accurately determined to match the image dimensions i will correct the code and try again right well it doesn't really matter if it's not being able to return the absolute value because it can't read those little numbers which again should have been just bigger the y-axis numbers should still give you like the relative size yeah it got an error again it appears there was another indexing issue due to the x-coordinate exceeding the image width this is likely because the estimated x positions for the center of the bars are not aligned with the actual image's coordinates but proceed correctly we need to accurately determine the x coordinates within the bounds of the image dimensions i will make the necessary adjustments and try again well right it's kind of hopeless you can you know it's not gonna succeed doing this many reasons like the original image is really the original figure is really bad should we try this one probably not yeah the colors come on the color should have been different just bad bad bad what year was it from right this was from 2017 can try and have a more recent paper it was comparing eg again an oddball task comparing eg from this headband and the 1020 eg cap some math does look just synthetic right this is different colors which is good again this is just five seconds more data to find if the raw data is available this could be turned into a graph fairly easily i guess it's over here yeah so this is in different bands comparing the two devices one good thing about this first study is that they did a comparison to the standard not just a reduced number of lectures here i'm not sure what they're doing i assume they just to reduce the number of lectures comparison the GPT might have better luck with this it's actually comparisons in different devices there's no chart for it GPT just make starting new shit to this table first see you can manage it gets the prices etc surprise this paper haven't turned this into a chart again infographic whatever get the paper let's do a quick i'm still loading quick summary they obviously there's more information on the muse you know if the paper is biased or just what's available still generating stuff or was it reading document why did it take so long network care the document signal quality investigation of a new wearable frontal lobe eg device focuses on a new wearable eg device named Mindy this study includes simulated signal tests and signal quality comparison experiments to assess the reliability and performance of Mindy the device has showed a stable and reliable hardware circuit with high correlation coefficients greater than 0.9 in simulated tests in comparison experiments with the gold standard device Neera scan Mindy demonstrated its ability to record high-quality eg signals in various tasks such as resting auditory oddball and attention tasks the study concludes that Mindy there's a promising eg recording device with potential applications in audiology cognitive neuroscience and daily requirements regarding the comparison of portable eg devices Mindy stands out with its high resolution compatibility with both dry and wet electrodes and Wi-Fi connectivity for data transmission it also offers features like impedance detection multi-device synchronization and event marking making it versatile for various applications the study highlights Mindy's advantages including its ability to provide raw data access high sampling rates and extended battery life which are essential for sleep monitoring and long-term eg data collection in research and cable settings the eg data sets generated for study described in the document are available upon request to corresponding author this suggests that while the data is not directly publicly available it can be accessed by contacting the author responsible for research yeah that's the graph for the the table there isn't it's an uh no it's not how to summarize this table visually we got a timeout first for today so might in a break um in the past was giving us like a one hour timeout this time it's only was it now seven yes seven minutes we can continue in seven minutes i don't know if we will keep getting those but yes it's actually more a bombarding the model get a timeout you can learn more about the timeout i've already submitted this in past usage cap request okay so the summary for this two papers is there yeah this is not a great paper at least the way it's published i haven't actually read the whole thing but yes say this graph is just the chart itself the graphics of it are really bad because you can well for example chipt4 could not extract anything out of it so close this one they have a paper yeah the graphs are better it's a newer paper as well so but this is not the whole data set with both papers the data is not publicly available there is only what's in some of the tables within the paper itself first part the first one didn't have any papers just read this bit just take a break integrated AFE to connect EEG data there are three data acquisition modes one transmit data to the computer in the local area network by the 32 Spanish Mercedes Wi-Fi module two save data in the sd card and three transmit data to the computer via usb the data can be saved on the computer in that format Mindy is powered by lithium battery and the battery life is about 12 h data recorded using a 1000 hertz sampling rate in 24 bit resolution figure one wearable frontal EEG device Mindy a hardware circuit b shape design and electro location of the wearable frontal EEG device Mindy and c circuit diagram of Mindy according to the EEG 10 to 10 system 7 ag silver 1 chloride dry electrodes with 9 millimeters in diameter fb1 fb2 f7 f8 fbz fd7 and fd8 are installed on the fixed position of the headband the shredding figure one b among the seven electrodes four electrodes fb1 fb2 f7 and f8 are connected to the analog input of AFE to record EEG signals the short circuit at fd7 and fd8 electrodes are used as the reference ref and the fbz electrode is used as the ground and Mindy supports both dry electrodes and disposable patch ag silver 1 chloride gel electrodes to collect signals these dry electrodes can be directly contacted with the skin to measure EEG or a patch electrode of matching size can be installed on the dry electrodes to measure EEG Mindy was worn on the forehead to collect frontal EEG signals 2.2 simulated signal test task in the simulated signal test task the arbitrary function generator AFG tectonics AFG 3102 beef of norgan usa was applied to simulate sine square and triangular waves with different frequencies 10 hertz 20 hertz and 30 hertz and amplitudes 40 the frequency of the simulated signal was in a frequency range of commonly recorded EEG since this AFG cannot generate signals with an amplitude below 1 millivolt the amplitude of the simulated signal was higher than that of normally EEG as shown in figure 2 the AFG was connected to the dry sensors of Mindy the simulated signal was used as the input signal of Mindy Mindy collected signals with 1000 hertz sampling rate figure 2 the connection between the arbitrary function generator and Mindy been a simulated signal test experiment 2.3 signal quality comparison experiments in this day 19 healthy students 8 females and 11 males were recruited from southeast university nanding china participated in the signal quality comparison experiment the average age of the participants was 22.79 plus or minus 2.57 years old all participants were informed about the experimental protocol and that his needing attention then signed the informed consent before the experiment this study was approved by IEC for clinical research of jungar hospital affiliated with southeast university IRB number 2019 ZDSYLL073P01 in this day during the signal quality experiment EEG signals of four channels fb1 fb2 f7 and f8 were recorded by the standard acquisition device Neuroscan tsunami two acquisition system and the wearable Mindy device at the same time Mindy collected EEG signals through disposable patch ag silver one chloride wedger electrodes ambu blue sensor and boron demar while Neuroscan collected signals through 10 to 10 system electrode cap with conductor paste to ensure that the EEG collected during the experiment had good signal quality alcohol and scrub gel were used to clean the forehead to reduce the impedance between the electrodes and the skin before the experiment before EEG acquisition the skin electrode impedance of both Mindy and Neuroscan dropped below 10 kilo ohms the wearing position of Neuroscan and Mindy was shown in figure 3a after wearing the Neuroscan's electrode cap as normal Mindy was worn below the electrode cap the EEG collected by Neuroscan used the 10 to 10 system EEG cap so the Neuroscan electrode positions defaulted to the standard 10 to 10 system by adjusting the wearing position of Mindy for the angle of the patch electrodes the electrodes of Mindy were as close as possible to those of Neuroscan the heads circumference and shape of each subject were not the same but after adjustment the distance between Mindy's patch electrodes and Neuroscan's electrodes was within 1 centimeter for both devices the sampling rate of the EEG signals was 1000 hertz and the references were fd7 and fd8 figure 3 signal quality comparison experiments a wearing position of Neuroscan and Mindy b resting state task c water tree old ball task okay obviously the reference is very important to suggest the references on the device itself assume the electrodes are on the forehead so they actually not in the same position as the 10 10 EEG montage yeah so there's lots of questions about this one again the raw data is not available you'd expect the data from all the electrodes in a raw shape and form without processing anything yeah I'm not sure why include a figure like that that could be just mentioned in text this yeah I don't think this one is of much use as well well yeah this is handy you want those actual recordings resting state let's see oh I it's I open I closes it so A is eyes close and B is eyes open instead of eye motion or a blink so essentially this has whatever that is I assume it's an eye movement that the the mind deep the portable device recording was a double the eye movement the amplitude which if you consider it to be your artifact it's not a great thing so here as well the question about these things is so yes sometimes the amplitude is greater but the question is what out of it is actual EEG so this amplitude fluctuations could be non EEG could be slight movement or things like that so yeah it could be good to look at the raw data I don't like summaries like this they tend to be somewhat biased in the way it's being processed yeah relative power to what so it's out of all the power but how come it's relative power here as well I get relative yeah this relative power just look in this graph a lot alone you will assume that all these numbers should add up to one so you have a point three five point two two five point two point one the chip the OCR and the image processing would be able to extract extract these numbers I suspect let's run down point three two point two five point two and point one does come to one okay but what's relative power in here the numbers available in those tables no and then there's a comparison table with all these different devices where they weren't actually tested in the study so show how it's that relevant yeah we'll spend more time on it if the raw data was available but it's not anyway we already spent too much time on this and we should be going to use it before as we do this quickly we have this two one 35 now also didn't tell me yeah the title of the previous chat so yeah when you put the NIH it's called NCBI papers you get the access denied by robots which is fair enough but yeah we now have till one 35 so the time it came us before was wrong let's try github co-pilot chat will explain this one I don't know if you need to do this or not explain this yes still have that error don't be so it's too easy 3.5 for now old model it's 3.5 so much faster it's crazy fast and we have this java script co-pilot explain this hoping them to 3.5 as well how much faster is this crazy fast and look at the cheat then we have the up by I could get up co-pilot it's using the ngp4 f4 it's slower so we'll html java script item call let's play this and let's also run item call no we don't want the virtual environment this is what we had yesterday so just select shell 10 it's only 100 samples what's a current a segment size is 40 that's why it's played two nodes but both nodes were 21 one day two 20 ones that's not isn't it's okay but it's yeah shell 10 if we plot the whole birthday g it keeps bounding with 21s some 27s in there 34 it'll stop there just by reloading the page yeah this are the frequencies questions how is the frequency mapping explain how the frequency mapping is happening in the gold it's the gpt 3.5 the same thing there's two main steps finding dominant frequencies mapping frequencies to midi numbers by the way I'm really wondering if flesche the trending fft analysis identifying dominant frequencies scaling to midi numbers and doing adjacent response so couple of questions are we doing dominant frequencies only chat gpt yes in a provided code the eeg data processing focuses on identifying dominant frequencies in the eeg signal segments specifically it calculates the dominant frequency for each segment using the fast Fourier transform fft technique the code does not capture or analyze multiple frequencies simultaneously within each segment the dominant frequency represents the frequency component with the highest amplitude within a segment of the eeg data it provides a single prominent frequency value that characterizes the signal in our segment if you want to capture and analyze multiple frequencies or frequency components simultaneously in the eeg data you will need to modify the code to perform more comprehensive frequency analysis such as identifying multiple peaks in the fft result or applying techniques like spectral analysis to extract a broader range of frequency information from the eeg signal can we find how many peaks are there in the frequency spectrum peaks yes that's what I meant fast Fourier transforms and converted into a frequency demand and then into a frequency where the dominant should is highest so I want to see if there are multiple frequency components in the data so that would be within each segment size I don't know if we should be plotting it because the tool really quite heavy might have a plot option turn on turn off so if the plots turn off the tool will be faster can we modify the code can we modify the code to find multiple frequency spectrum peaks so potentially f0 f1 f2 yes you can modify the code to find and analyze multiple frequencies in the eeg data including a fundamental frequency f0 and its harmonics f1 f2 etc to achieve this you will need to make several modifications to go adjust segment size you may need to adjust the segment size use for fft analysis larger segments can capture more frequency information but may result in reduced time resolution identify multiple peaks instead of finding only the dominant frequency be modify the code to identify multiple peaks in the fft result for each segment this would involve searching for and recording the frequencies corresponding to these peaks filter harmonics after identifying multiple peaks you can filter and analyze the harmonic frequencies f1 f2 etc by examining frequencies that are integer multiples of the fundamental frequency f0 these harmonic frequencies will typically appear as peaks at integer multiples of the fundamental frequency store multiple frequencies store the frequencies corresponding to the identified peaks in each eeg data segment allowing you to capture multiple frequencies in a spectrum here's a high-level example of how you might modify the code python copy code hash modify the get underscore dominant underscore frequencies function to return multiple frequencies def get underscore frequencies eeg underscore data sampling underscore 8 segment underscore size equals 40 frequencies equals hash divide eeg underscore data into segments and analyze each segment for high in range 0 then eeg underscore data segment underscore size segment equals eeg underscore data ii i plus segment underscore size if then segment less than segment underscore size break hash skipping complete segment harsh to trend the segment the trended underscore segment equals to trend underscore data segment hash up by fft to the The Trinidad segment FFT underscore result equals NB dot FFT dot FFT, the Trinidad underscore segment FFT underscore for Q's equals NB dot FFT, FFT for Q, then, the Trinidad underscore segment D equals 1, something underscore 8. Hush extract the magnitudes FFT underscore magnitudes equals NB dot FFT. Hush modify the converted E to music root to return multiple frequencies. Hush modify the scale underscore E underscore underscore midi function to handle multiple frequencies. Please note that these modifications provide a general idea of how you could adapt the code to capture and analyze multiple frequencies, including harmonics. The specific implementation details may vary based on your requirements and the characteristics of your EEG data. So see what GitHub cupart says, suggesting another function, we have catch frequencies already now. So how many top frequencies is it finding, finding some of the topics, so how about one, two, three, and then play them as separate notes, in a different suggestion, but I think this one actually will actually work, number is preset somewhere, isn't it? Please note that these modifications provide a general idea of how you could adapt the code to capture and analyze multiple frequencies, including harmonics. The specific implementation details may vary based on your requirements and the characteristics of your EEG data. The code can be used to identify multiple peaks, instead of finding only the dominant frequency peak, modify the code to identify multiple peaks in the FFT result for each segment. This will involve searching for and recording the frequencies corresponding to these peaks. Just quickly again, so I want to try it quickly. It will be quick to implement. Just quickly, I should have done. In this modified function, find underscore multiple underscore frequencies, the code identifies peaks in the FFT magnitude spectrum and extracts the corresponding frequencies. You can adjust the threshold parameter to control the sensitivity of peak detection. We have one of five that peaks, and then take three of them, but we shouldn't be assuming there are three peaks, or should we, three peak frequencies in the spectrum separate and play together. Yes, you can modify the code to find multiple peak frequencies in the spectrum and then play them together as a chord. Here's a high level outline of the modifications you can make. Modify the frequency analysis function. Adjust the get underscore dominant underscore frequencies function to find the n highest peaks in the FFT result instead of just one. You can use functions like arg partition or arg max with appropriate conditions to identify the indices of the highest peaks. Extract multiple dominant frequencies. Modify the code to extract the frequencies corresponding to the identified peak indices. Store these frequencies in an array or list. Scale frequencies to midi numbers. Use the frequency underscore underscore midi underscore number function to scale each of the extracted frequencies to midi numbers. Play the chord. Once you have the midi numbers representing the multiple dominant frequencies, you can play them together as a chord. You may need to use a library or tool that supports playing multiple midi notes simultaneously. Here's a simplified example of how you can modify the code to play three dominant frequencies together as a chord. Python, copy code hash inside the convert e to music root. Hash modify decide to find the n highest peaks instead of one peak underscore indices equals nb arg partition FFT underscore magnitudes nn. Hash. In this example, n represents the number of dominant frequencies you want to capture and play as a chord. You may need to adjust the code further to handle user input or other requirements specific to your application. We're going to be using a play the way and now let's change this for reference only. Let's modify this quickly, can modify the code to find three peak frequencies in the spectrum and then turn them into three separate notes played together. Currently, we have one note at a time. Window size of 100. Pretty sure we have the segment size at 40, that's why we're getting two notes. Should be a middle of a seizure. Okay, okay, okay, okay, okay. First, it's horrible. Well, that's why we're looking at chords, looking at chords. Update the get underscore dominant underscore frequencies function to return the top three dominant frequencies instead of just one. You can achieve this by sorting the dominant frequencies into sending order and selecting the top three. Yeah, top three, yeah, this one is three, they would not be played to key ever, they'd be played one after another. Modify the convert underscore eq function to handle the three dominant frequencies and convert them into three separate MIDI numbers. You can then play these MIDI numbers together as a chord. With these modifications, the convert underscore eq function will return three MIDI numbers corresponding to the top three dominant frequencies found in the EEG data. You can then play these MIDI numbers together as a chord. Yeah, okay, how do I do that? First of all, because we're making so many changes, let's do some saving. Well, this tool definitely works, but yesterday, get rid of all these copies as we're changing the bottom chord, particularly the get dominant frequencies, we'll get the three of them at once, dominant frequencies, FFT, sampling rate, FFT, yeah, this bit, so we're getting that top three in the list, still returning dominant frequencies to the output should be the same to modify, convert the EEG and the sampling rate to the next 10 and part of the MIDI numbers. This will stuff up my code, give it a go anyway, the HDS-2200 doesn't work, so it's supposed to be rewritten elsewhere as a default, it must be in the HTML, even if you play at least 400, that will be one second worth of data, and inside we should have 10 notes. If one player did actually change something on the page, so it's a second different channel. Yeah, okay, that problem, so if I click this, this is an unrelated problem, I use a scroller to make a cheat data once, and wipe does it twice, change the code, play MIDI numbers together as a code, also get the A when using the scroller, twice, get off once only. To play the MIDI numbers together as a code, and to prevent notes from playing twice when using a scroller, you can make the following modifications to code, update the play sequence function to accept the list of MIDI numbers and play them together as a code, you can do this by calling the play method for each MIDI number simultaneously. I already have a play sequence, no, why not, how do we play, how many functions do we have, something wrong with this search, do I have nine definitions, trend data get dominant frequency, scale to MIDI, frequency to MIDI, load data, load data, we're already playing with the HE. Update the play sequence function to accept the list of MIDI numbers and play them together as a code, you can do this by calling the play method for each MIDI number simultaneously. Yeah, but we don't have this function, let's have piano ready, do we have piano ready somewhere, it's not in JavaScript, are you sure this is Python, this is JavaScript, isn't it, play sequence, yes we do have play sequence, yeah my mistake here, this should be JavaScript, not Python, to update a JavaScript, let's see, MIDI numbers, it's the same, the index to create another constant to hold all the oscillators for the chord, let's just try that, function looks complete, we need the frequency, do I have this function now, let's do it by the code, so we have frequency to MIDI number, with these modifications, when you call the play sequence function with a list of MIDI numbers, you will play them together as a chord by creating multiple oscillators for each note and stopping them after a short duration, this should prevent notes from playing twice and play them together as a chord, adjust the duration and oscillator settings as needed for your desired sound, this is a stick, let's test that again, then let's take here JavaScript as this function, 78, that's actually, a different line is it, 77, 15, while it sounds more likely, MIDI 2 frequency, and this work, piano, dotted with all notes, that's nice, yeah that's very wrong, isn't it? for these modifications, when you call the play sequence function with a list of MIDI numbers, you will play them together as a chord by creating multiple oscillators for each note and stopping them after a short duration, this should prevent notes from playing twice and play them together as a chord, adjust the duration and oscillator settings as needed for your desired sound, it's something very wrong, so for window size of 400, expect, I expect then chords, chords, play one after the other, one after the other, yep also are the chords made of notes or frequencies, yep why do I need another function, can we just modify one of the existing functions? you can certainly modify one of the existing functions to play chords sequentially without creating a separate play chord sequentially function, here's an updated version of the play sequence function that achieves this, in this modified play sequence function the promise chain is created directly within the function and it plays the chord sequentially waiting for each chord to complete before starting the next one, the result function is passed to the play chord function to signal when the chord has finished playing ok, we don't actually have the play chord function as well, but we need a play chord, can we just modify existing functions? certainly, you can modify the existing functions to play chords sequentially without introducing additional functions, here's a way to achieve sequential chord playback by modifying the play sequence function still has play chord in it, what's in play chord again? play chord, we have a modifying play sequence, it's here it seems like you've integrated the chord playing functionality in the existing code, here are the modifications you made to the code to include the chord playback functionality yep, so it's here you can add some debugging statements to your code to help identify the issue for example, you can add a console.log statement before the free tube to log the value of MIDI numbers in a bit, 3 MIDI numbers for each segment this JavaScript code is part of a web application that transforms EEG, electro and cephogram, later in the music here's a brief overview of its key functions audio context management, get audio context, creates an audio context for handling audio operations resume audio context, resumes the audio context if it's suspended, ensuring continuous audio playback piano sound loading, load piano, uses the sound from library to load piano sounds it sets a global piano object in the flag piano ready when the piano is loaded fetching EEG data, fetch EEG data, start, count, channel, fetches EEG data from a serve endpoint, with parameters for data start, count and channel data update and plotting, update data, updates the EEG data based on user interactions like slider movements or channel selection it then plots the EEG data and converts it to music EEG data visualization, plot EEG data, EEG data, plots the EEG data using chart, yes converting EEG to music, convert EEG to music, start, count, channel, converts EEG data into MIDI numbers this data is then used to generate music music playback, the functions play chord, MIDI numbers, then play sequence, chords, play musical chords and sequences, respectively MIDI to frequency, MIDI number, converts MIDI numbers to frequencies user interactions a band listeners are set up for playing MIDI sequences and for debouncing the data update function, ensuring smoother user interaction the bouncing function, the bounce, funk, weight, or media, is a utility function to limit the rate at which a function can fire this script combines web audio API, data fetching, and interactive visualizations to create an interesting application that turns brainwave data into music this python script is for a flask web application that turns EEG data into music it consists of several key components flask application set up, the application is set up using flask or web framework for python roots are defined for serving the main page and handling specific requests like fetching EEG data and converting it to music roots renders the main page, index, 8tml get EEG data, fetches EEG data based on provided parameters like start, count, and channel convert EEG to music, converts EEG data into MIDI numbers suitable for generating music data loading and processing load underscore data, start, count, channel, loads EEG data from a text file, selecting a specific channel and range the trend underscore data, data, removes linear trends from the data, preparing it to frequency analysis get underscore dominant underscore frequencies, EEG underscore data, sampling underscore 8, segment underscore size determines the dominant frequencies in segments of EEG data using fast Fourier transform, FFD music conversion frequency underscore to underscore MIDI underscore number, frequency, converts frequencies to MIDI numbers scale underscore EEG underscore to underscore MIDI, EEG underscore frequencies, scales the EEG frequencies to MIDI numbers ensuring they form within a standard piano range configuration and constants, the script sets various constants such as the sampling frequency, FS, and file parts for EEG data error handling, the roots are equipped with tri accept blocks to handle exceptions and return appropriate responses main function, the application is started with app.run, debug equals true, in the main box, enabling debugging mode for development purposes this application integrates data processing with web technology, showcasing an innovative way to visualize and interact with EEG data it's designed to read EEG data, analyze it, and transform it into a musical format, creating a unique auditory experience based on brainwave patterns the error type error, MIDI numbers, for each is not a function, suggests the MIDI numbers, which is expected to be an array there's not an array format where it is passed the play chord function this issue typically arises when the expected input format doesn't matter the actual data being received let's examine a couple of potential causes and solutions incorrect data format, ensure the MIDI numbers is indeed an array can we make sure for each segment we find three peak frequencies and then translate them into three nodes and then play them as a chord you can modify the get underscore dominant underscore frequencies function to find the top three peak frequencies in each segment and then translate these frequencies into nodes to play them as a chord here's an approach to achieve this modify get underscore dominant underscore frequencies function, update this function to return the top three frequencies for each segment instead of just one dominant frequency sure I changed this already, honestly, you know what make sure the play chord and play sequence can handle an array of arrays, where each array represents a chord, an array of three MIDI numbers yes we don't have these functions make sure the play chord and play sequence can handle an array of arrays, where each array represents a chord, an array of three MIDI numbers converting frequencies to chords, you'll need to dust the scale underscore eg underscore underscore MIDI function or add a new function to handle the conversion of three frequencies to a chord, an array of three MIDI numbers integration, make sure that the data flow from the EEG data processing, Python, and the music playback, JavaScript, greatly handles this new structure of data, particularly the format of the data sent from the flask after time by implementing these changes, your application will be able to find the top three frequencies in each EEG data segment and translate them into chords for playback MIDI to frequency conversion, MIDI to frequency, MIDI number, converts a MIDI number to its corresponding frequency chord playing, play chord, MIDI numbers, takes an array of MIDI numbers, lengths should be three for chord, and plays them simultaneously oscillator creation, for each MIDI number, an oscillator is created with the corresponding frequency gain node, a gain node is used to control the volume of each note start and stop, each oscillator is started immediately and stopped after one second, you can adjust the duration as needed error handling, the function checks if MIDI numbers is a valid array with exactly three elements remember, you need to have an audio context available and make sure the browser's auto-play policy is handled, eg, requiring user interaction before playing sound this function should be integrated with the rest of your application where MIDI numbers are generated and played as chords sequence validation, this function first checks if chords is a valid array of chords, where each chord is an array of three MIDI numbers chord duration, chord duration specifies the length of time each chord is played it's set to 1000 milliseconds, one second, it can be adjusted playing chords sequentially, the function uses a recursive approach with set timeout to play each chord after the previous one has finished iteration over chords, play next chord is a recursive function that plays each chord in the sequence and schedules itself to play the next chord after the current one has finished starting the sequence, the sequence of chords is started by initially calling play next chord this implementation assumes that play chord plays each chord for exactly chord duration milliseconds adjust chord duration as needed based on our application's requirements and the implementation of play chord this function should fit into the broader context of your web application where the sequence of chords is determined from the eg data she could somehow jump between GPT-4 and 3.5 because obviously some things are simple and could be much quicker addressed by 3.5 so generally in the code are we creating MIDI notes but then eventually turning them back to frequencies? is that correct? yep ok that was a sound, it was horrible frequencies to MIDI notes these extracted frequencies are then mapped to MIDI notes MIDI notes are a standard way to represent musical notes in the digital world each MIDI note corresponds to a specific frequency the mapping is done using the formula after converting a frequency to a MIDI note number this step is necessary because it's easier to work with MIDI note numbers for musical purposes like ensuring the notes are within a certain musical scale or range MIDI notes are frequencies for playback when it's time to play these notes the MIDI note numbers are converted back into frequencies this is because in the end you need actual sound frequencies to play music the web audio API which you are using for playback requires frequencies in HZ to generate sound using oscillators the conversion from MIDI notes back to frequencies is done using the standard formula that mapped MIDI note numbers to their corresponding frequencies in summary the process starts with the eg data, biological signals, transforms these signals into a musical representation, MIDI notes and then converts these MIDI notes back into sound frequencies for playback this approach allows for an interesting and creative way to interact with the eg data, turning it into a musical experience ok, so the conclusion is for now sounds very horrible try playing with some more the other thing is pretty sure we have to increase the segment size it's not chess, is it? no, python what is it? segment size, no, it's 40 yeah, this segment size should be longer if we make it about 100 we will run on the page and we do the whole segment so this should have the seizure onset and after the seizure sorry for that, I have to reduce the volume it's pretty horrible to start this page those are the frequencies that meant to be translated into notes into chords each three, so that would be your dominant frequency and essentially F1 and F2 so meant to be finding three peaks in the FFT in the spectrum will work by some of them just repeat themself, does it mean it couldn't find the third peak or any of them different like having three different components no, they'll have one and two of the same sure, why so if the segment size is 400 essentially will generate a chord every second let's try that quickly there should be less of them, there's chords let's try the whole thing I don't need to remap this those frequencies generally working yeah, any suggestions comments screaming at me presenting something horribly wrong do let me know and I'll see you next time