 two, three platforms at the same time, see how that goes, just check a few things, yeah just checking my internet speed, okay let's get to it, we will continue from where we left off, so we have this last application that we're developing, it currently looks like this, we have this synthetic EEG that we're scrolling through, and every now and then we have this synthetic EMG coming through, and we're making a detector that will determine the likelihood of the EEG signal being contaminated by EMG, divided by this thing, if anyone wants me to talk more about that I'm more than happy to, this is nothing we're developing, turning hand gestures into music, I'm more than happy to talk about that as well, we have actually now a version for it, and that we haven't released and might just mute this side so it doesn't bother us too much, I'm actually, whoops, to keep it intact, keep it intact, so my video works, let's say, I wish everything was coming through to OBS, I'm using OBS for stream, but no, I only have some of the YouTube stuff coming through, but not, was it LinkedIn and Twitch, so let's jump into it, we're having this errors before, so again we have this EEG, this synthetic EMG, we can scroll through it, I can hear my CPU complaining of it, it's not using mostly GPU, we're trying to fix this thing where when you scroll it makes, I don't know where that came from, we'll later turn it into a feature, instead of it being a bug, but currently we have the synthetic EEG and EMG artifact every now and then, so it could be like you're chewing during EEG recording or anything like that, and we're making a detector that will tell us the likelihood of the EEG being contaminated by EMG artifacts, so we have everything here, ChageBG4 is writing prompts or GitHub co-pilot for us, so essentially, yeah, we're trying to minimize backend use, but that didn't work, so we'll leave it as it is at the moment, at the moment all the fuzzy membership functions are happening on the server side, that's okay for now, we'll have to change them as well, we're actually trying to transfer them into, so let's see just the sanity check, the frequency 17.4, 17.3, yeah, it should be lower between, yeah, so they come every 10 seconds or so, it's going 18, wasn't it 16, yeah, I can't really see those numbers, they are green on black, we'll change that as well, so it should be knowing EMG there, the frequency, we expect the frequency to be lower, the amplitude 0.86, 0.87 bugger all year that EMG artifact is not much bigger than the background, than the actual EEG waveform, we have to fix all those, just separate this too quickly, still need a code, back to the original code, there are a couple of things, the membership functions are still not showing anything, we need better prompts for GitHub co-pilot, but please look at the code I provided in the beginning of this chat, let me know if you still have access to it, if not I'll share it again, now the good thing that current amplitude and current frequency are changing as I scroll through the synthetic data, however, the current artifact risk always remains the same, it's always 0.89, we need to fix all of those, also a minor thing that the EMG artifact is not, could be made more, slightly more visible, not handling the data as expected, ensure the data structure sends a chart matches what's expected by chart, JS, if you're using arrays of points, ensure they're in the format x1, y1, x2, y2, check that the data from the server is received correctly and that the JavaScript function plot membership functions is called with the correct parameters, review any console errors that might indicate what's going wrong, if the artifact risk is not updating, it could be because the same data segment is being analyzed repeatedly or the risk calculation isn't receiving updated input parameters, verify that the analyze and display EEG function receives different data segments upon scrolling, ensure that the artifact risk calculation in the back end uses the correct, updated values each time it's called, to make the EMG artifact more visible in the synthetic data, increase the amplitude of the EMG signal within the generate underscore synthetic underscore EEG function, adjust the frequency range if necessary to make the artifacts distinct from the EEG signal, here are refined prompts for GitHub co-pilot to address these issues, prompt for displaying membership functions, Arduino copycode, GitHub co-pilot, I have membership function data that needs to be displayed using chart, JS, but it's not showing up correctly, the data is structured as arrays of x, y, points, can you suggest JavaScript code to correctly display these points in a line chart, prompt for updating artifact risk dynamically, prompt for making EMG artifact more visible, try using these prompts with GitHub co-pilot to get more precise suggestions for your code, adjust the prompts based on the feedback and code suggestions you receive from co-pilot and keep testing your application after implementing each change to ensure functionality, okay, let's do that, so this one, the first one, so it's for JavaScript code, we just select all of it, obviously we don't need to refer to GitHub co-pilot as GitHub co-pilot and says I have a membership function data that needs to be displayed using chart, JS, let me double check that, yeah, we're using chart, JS, but it's not showing up correctly, the data structure is arrays of x and y points, okay, we'll assume that's correct, can you suggest Java code to correctly display these points, can you suggest how to change the JavaScript code to correctly display this, to just get deleted on me, do it again quickly, how to change to that, so it's actually giving me code, yeah, suggesting to change plot data, could be simplified, mapping over the dataset points twice, didn't realize I'm doing that, it's just giving an error, if there are no points, then there is mapping, oops, no wait, that's mapping the labels and separately, so when I, now that looks legit, but okay, what's the suggestion, so we have plot data, canvas ID, windows, chart destroy, yeah, it's if everything was plotted before, it will destroy that, just an error check, okay, we still have the two maps checking for current value, or considering what we currently have isn't working, set five, it's data plot, current value, this modification, I replace the separate labels and memberships array with a single chart that array, each element of chart that is an object with x and y properties, okay, now I'm getting an error, so I'm getting an error before, make it up co-pilot, it's not great if it will keep making mistakes, we might, that new chat, a new session with GPT4, tell me to start troubleshooting, don't really want to do, yes, we're having trouble with determining the structure of the data, it should know, do you know what the structure of the data is, get the code, it says based on provided code, provided a sample of the data prior as well, yeah, we have the data in this, you have no idea why it's structure seems, why it's not just a simple array, it's like way too complicated, let me know what you think, how do you use it, how do you use GPT or GitHub co-pilot, but I say, I suspect GitHub co-pilot won't be helping me here, so I have to start the new chat, yeah, we're using chart.js, we have a selection for new sections for membership functions, graph current values, get rid of that, yeah, we have the description at the bottom that we need to rewrite eventually as well, just quickly a format document, when we say that more code is coming the next prompt, hopefully it doesn't ignore it, put like exclamation marks there or something, because I do, it's like 50-50, it will ignore it or not, looks like you're working on a detailed HTML page for your project, the code you've provided is pretty well structured, it includes sections for meta tags, star sheets, the body with various HTML elements, and some JavaScript for functionality and analytics, if you have specific questions or need help with certain aspects of the code, feel free to ask, we have that, then we have the JavaScript, just quickly, yeah, this is an attempt to do more stuff on the front end, a user side that didn't work, might get rid of that, yeah, we have two global variables, yeah, we have quite a bit of debugging, a validator structure of the membership that, so it meant to be array membership data, then it should have amplitude, frequency and artifacts, so just a recap, we are in this eg, data side, so we're extracting amplitude and frequency, and based on that, we make membership functions that will essentially determine if this segment of the data is, has to what degree, it has EMG in it, so right, the chart already exists, I'm gonna destroy it, yeah, that's just clearing the chart before plotting something else, and that's okay, let's just format document, it's the same, and we want to make sure it doesn't ignore this prompt at the end, that the, a Python code mask app is coming next, exclamation marks seem to help, great, the JavaScript code you've provided handles the front end functionality for fetching and displaying EEG data, as well as interacting with the back end, presumably a Python Flask app, as you mentioned, the code is neatly organized and seems to cover, data fetching and error handling, fetch EEG data function to fetch EEG data from a Flask endpoint with proper error handling, data normalization and analysis, functions like normalize and calculate average amplitude for processing the EEG data, data visualization, display EEG data and plot membership functions for displaying EEG data and membership functions on charts, dynamic content updates, functions to update the displayed data based on user interactions, like scrolling through the EEG data, back end interaction, sending processed EEG data to the back end for further analysis and receiving results to display, this setup should work well for an interactive, data-driven web application. I'm curious to see how you've implemented the back end in Flask, as it will complete the picture of your application's architecture. Feel free to share. Right, should we ask co-pilot to remove all the comments, but essentially we're defining the fuzzy variables, and finding the membership functions, so those we'll see in a second how they look like, they're meant to be this trapezoid, yeah trapezoidal membership functions, so essentially the threshold for each parameter, so we have essentially for amplitude low, medium and high and frequency low, medium and high, and then the artifact, I don't know what's called RISC, I don't like the name RISC, but we'll stick with it for a while, because it just works, if I change the name I'll probably stop something else up, and we have those rules, so essentially those rules should have names to them. Ah right, okay so they essentially say that if the amplitude is low and frequency is low, the artifact risk is low, if the amplitude is medium, frequency is medium, the artifact is medium, and if the amplitude is high, frequency is high, the artifact risk is high, okay as you can imagine it's not the greatest detector, but just an example we'll be improving on that later on, and then we're creating, actually we might leave those comments, then creating the control system, we'll look into it later as well, and this probably does the, I don't know, that's someone we're doing the defazification, yeah we have two filters, let's actually know why do we need the filters, really tempting getting rid of the filters to keep them there, well because this data is synthetic, so so instead of filtering we could just adjust how you know the data is being generated is to this, your Python Flask code integrates well with the JavaScript frontend you've shown earlier, here's a quick breakdown, fuzzy logic setup, you've defined fuzzy variables, amplitude, frequency, artifact, underscore risk, and their membership functions using the sk fuzzy library, this setup is crucial for your EEG analysis using fuzzy logic, EEG data generation and processing, generate underscore synthetic underscore EEG and preprocess underscore EEG underscore data functions handle synthetic EEG data generation and preprocessing, which includes normalization and frequency extraction, EEG analysis, analyze underscore EEG function takes processed data and applies your fuzzy logic control system to determine the artifact risk, the root root serves the html page, generate underscore EEG, generates and returns synthetic EEG data, analyze underscore EEG, processes and analyzes the EEG data sent from the frontend, get underscore membership underscore functions, provides the membership functions for the frontend to display, error handling, your use of tri accept blocks for error handling and returning appropriate http status codes is good practice, data validation and logging, validate underscore and underscore log underscore data function adds an extra layer of data integrity checks and logging, which is great for debugging, this back end complements the frontend by providing the necessary data processing and analysis logic, the integration between frontend and back end seems smooth with clear json communication for data exchange, looking forward to the next part of your code, the error message suggests that there's an issue in the plot membership functions function in your javascript code, specifically related to how the data set points are being processed or accessed, given the error message, it seems that the data set points property is either undefined or not structured as expected, let's review the relevant parts of your javascript and flash code to diagnose and suggest a fix in your plot membership functions function, you're expecting each data set in the amplitude data, frequency data and artifact risk data arrays to have a points property, this error might occur if the structure of the data received from the flashback end is different from what the frontend expects, in get underscore membership underscore functions route, you're sending membership function data structured as follows, where each asterisk underscore mfs is an array of dictionaries with name and point skis, data structure mismatch asterisk j, check ensure that the data structure returned from the flashback end matches what the frontend expects, the points property should be an array of x, y, pairs, fix if there's a mismatch adjust either the frontend or the back end to ensure consistency, check make sure that the data is correctly parsed when received by the frontend, sometimes issues can arise if the data is not properly parsed as jason, fix you can add a console log right after fetching the data in plot membership functions to inspect the received data structure, check network issues or incorrect routes can cause communication problems between frontend and back end, fix verify the network requests in the browser's developer tools network tab to ensure that the request to get underscore membership underscore functions are successful and returning the expected data, here's a snippet to add a debug log in your plot membership functions function, this log will help you see exactly what data structure you're receiving and whether it aligns with your expectations, once you identify the mismatch you can adjust either the frontend or back end code accordingly, from your provided output the amplitude data is structured as an array of three arrays each containing 11 pairs of values these pairs represent points on a membership function curve, here's a breakdown of the structure, outer array contains three elements each corresponding to a different membership function e.g. low, medium, high, inner arrays each of these arrays contains 11 pairs sub arrays of values value pairs each pair represents a point on the membership function curve with the first value being the x coordinate and the second value being the y coordinate this structure is typical for representing fuzzy membership functions where each function is defined by several points on a graph to simplify you can transform this structure into a more readable format such as an array of objects where each object represents a membership function with its name and points in this format each membership function is an object with a name and an array of points where each point is an object with x and y properties this structure is easy to understand and work with especially when rendering these functions on a chart or processing them further you can apply this simplification to the frequency and artifact risk data as well ensuring consistency across your data structures yeah with those other names low, medium and high it's okay so we have convert membership yeah we don't want to convert it and no we don't need to convert it we can generate it differently on the server side but we don't want to add another function to convert it the main problem is that these membership functions are not being currently displayed on the chart correctly the charts are empty yep if the membership functions are not being displayed correctly on the chart and you want to address this without changing the data structure on the server side we should focus on the client side code handling the chart display given the data structure you've provided let's revise the plot membership functions function in your javascript code the key is to ensure that the data for each membership function is correctly mapped and fed into the chart verify chart initialization ensure that the charts are initialized correctly in the plot membership functions function data mapping make sure that the data for each membership function is properly mapped from the received structure to the format required by the chart library chart data and options confirm that the data and options provided to the chart are in the correct format logging and inspection add console logs to inspect the data at various stages here's a revised version of plot membership functions with added console logs for debugging after implementing the revised code check the console logs in your browser's developer tools this will help you verify if the data is correctly processed for chart if the charts are still not displaying correctly inspect the format of datasets in the plot data function to ensure it aligns with what the chart js library expects make sure that the canvas elements for the charts are correctly referenced and that the dimensions are properly set verify the chart js is correctly included and initialized in your project these steps should help you identify where the issue lies in displaying the membership functions on the chart it doesn't seem right let's say bottom it's the last function that we have so we have plot data a we can comment out the previous function right no it's actually could make sense because the plot data before it was a bit inside the plot membership function now it's a separate function that could actually write it's actually works yeah it actually works because they need to fix the colors but yeah those are the trapezoidal membership function this one looks triangular it's not giving me some fake stuff is it there's trapezoidal and triangle in between is that the case trap now they meant to be all trapezoidal okay i know why because this number is the same so it actually yeah makes a triangle so this point five no that's okay okay that actually worked and not sure what the original problem was but thanks for fixing it great to hear that it worked sometimes the issues with data visualization can be quite supple and a fresh look or a different approach to handling the data can resolve them it's possible that the original problem was related to how the data was structured or interpreted by the charting library if you have any more questions or run into other issues feel free to ask happy coding the artifact risk is always the same value can we make sure it's being calculated over the displayed window and not the entire set yep if the artifact risk value is always the same regardless of the displayed window it suggests that the calculation might be inadvertently based on the entire dataset rather than the specific segment currently displayed to ensure the artifact risk is calculated for the visible segment only you need to modify how data is processed and sent to the back end for analysis here's a strategy to address this ensure correct data segment is sent for analysis modify the analyze and display eeg function in your javascript code to send only the currently visible segment of eeg data to the flask back end for analysis back end processing ensure your flask back end analyze underscore eg underscore root function correctly processes the received segment for artifact risk analysis here's how you can adjust your analyze and display eeg function in the eeg scroll container event listener ensure that analyze and display eeg is called with the currently visible segment of the eeg data this adjustment ensures that each time the user scrolls through the eeg data only the visible segment is analyzed for artifact risk leading to more accurate and dynamic risk assess it's first of all we can get rid of all that and now in this async function analyze and display eeg data we try to ensure only the visible data segment send for analysis okay that's the same eeg data segment try a constant response weight fetching analyze eg method pause headers content type application json body json stringify eeg data which is eeg data segment and we catch catch console error okay in an eeg scroll container event listener ensure that analyze and display eeg is called with the currently visible segment of the eeg data right right so we have the container at the bottom that's responsible for scrolling through the data existing code calls call analyze display eeg with the visible segment if visible segment length total data points display eeg data okay visible data segment analyzing display eeg visible data segment okay pretty sure it's exactly the same play say visible segment okay maybe that value and maybe that value is being recalculated but it just stays the same I already have the same code as you suggesting please double check and the changes you suggested are essentially keeping the code exactly the same another thing is is it possible that the artifact risk actually is being recalculated as I move the scroller however the artifact risk value remains the same would that mean if that's the case does it mean we have to modify membership functions yep the membership functions are normalized if your code is already structured as I described and the artifact risk value remains constant despite different data segments being analyzed there are a few possibilities to consider consistent data characteristics it's possible that the different segments of your eeg data are quite similar in terms of the features amplitude and frequency relevant artifact risk calculation if the segments don't differ significantly in these features the artifact risk value calculated by the fuzzy logic system might indeed be consistently similar membership function configuration if the eeg data segments do vary significantly and you're still getting constant artifact risk values it might suggest that the membership functions and rules in your fuzzy logic system are not sensitive enough to these variations you might need to adjust the membership functions or rules to better differentiate between varying data characteristics data processing and analysis accuracy ensure that the preprocessing of eeg data like normalization or feature extraction is correctly implemented and the features are accurately representing the characteristics of each data segment to investigate further you can log and inspect data add console logs in your javascript code and print statements in your flask app to inspect the amplitude and frequency values being calculated for different data segments this will help you understand if these values are indeed varying as expected review membership functions and rules revisit the configuration of your membership functions and rules in your flask app it may be helpful to log the input values to the fuzzy logic system and their corresponding outputs to see if adjustments are necessary test with varied data if possible test your system with a different data set known to have varying characteristics to see if the artifact risk calculation response is expected adjusting membership functions or rules would require a deeper understanding of the specific characteristics of your eeg data and how they should map to different levels of artifact risk this might involve some trial and error or consultation with domain experts in eeg analysis okay if you are a domain expert and you would like to contribute uh do contact me or contact or prompt the bot directly that's okay too okay one thing so uh yes this is the case that as i scroll through the synthetic data set the amplitude doesn't change much can we potentially reduce the amplitude of the baseline eeg the frequency doesn't change much as well also the current frequency is between 16 to 17 hertz which is outside the range is it a problem if the amplitude and frequency of your synthetic eeg data do not vary much as you scroll through the data set and the frequency is consistently outside the expected range these factors can indeed contribute to the constant artifact risk values here's how you can address these issues reducing the amplitude of the baseline eeg in your synthetic data generation function could increase the relative impact of other variations like artifacts on the calculated features in your generator underscore synthetic underscore eeg function you can adjust the amplitude of the baseline signal this code reduces the amplitude of the baseline eeg signal you can experiment with different values to find a balance that creates more variability in your data if the frequency is consistently outside the expected range it's important to ensure that your frequency calculation method is appropriate and that your synthetic data generation aligns with the expected frequency range frequency calculation check how you're extracting the frequency feature from the eeg data ensure that the method used eeg fft is appropriate and implemented correctly synthetic data generation adjust the frequency components in your generator underscore synthetic underscore eeg function to ensure they fall within the expected range for instance if you want to simulate alpha waves typically 8 to 12 hertz you can adjust the synthetic signal generation to reflect this python copycode hash adjusting frequency components file for waves eeg underscore signal equals 0.1 asterisk np dots in 2 asterisk np pi asterisk 10 asterisk t plus np random normal 0 0.02 length hash 10 hertz alpha wave if after making these adjustments the artifact risk value still doesn't change as expected you might need to revisit your fuzzy logic systems membership functions and rules they should be sensitive enough to the range of amplitude and frequency values expected in your data this might involve adjusting the ranges or shapes of the membership functions to better match the characteristics of your eeg data by making these adjustments and testing with the modified synthetic data you should be able to observe more variation in the artifact risk calculation as you scroll through different segments of the dataset okay we need to clear we need to keep updating the membership functions but first can we improve on the signal there are a couple of things if the EMG artifact is coming every 10 seconds they can make sure the window displayed on the screen is say five seconds this is to make sure that we have sections displayed without any EMG in them to improve the synthetic eeg signal and ensure variability in the displayed segments especially with regards to the presence and absence of EMG artifacts you can adjust the generate underscore synthetic underscore eeg function the idea is to generate data where EMG artifacts appear intermittently allowing some segments of the eeg data to be displayed without any EMG interference you can modify the function to include EMG artifacts less frequently if your display window is five seconds and the eeg data is sampled at 250 hertz this window represents 1250 data points to ensure the some segments do not contain EMG artifacts you can adjust the frequency of EMG artifacts to occur less often than every five seconds let's adjust the EMG artifact frequency in this updated function EMG artifacts are inserted every 10 seconds 250 data points ensuring that there are segments yeah we'll have to continue this next time please check balikios.com let me know what you think bye for now