 It seems like we've had an internet dropout, still not live, are we? Now we are, guess we, back to where we ended before. We always get the same nodes playing, which is not great. App nodes, what is this? Even after automatically varying a mean and max power, use some help with this one. Remain the same after normalization region, suggest that the issue might not be with the normalization process, but rather with the how EEG data is being interpreted and all mapped to musical nodes. Review the EEG variability, yes it does, very quite a bit, control F5, and varying the volume, which will vary the volume of your amplitude, yeah, that kind of makes sense. So there is enough variability. Look closely at the map EEG data to nodes function. Ensure that it's designed to translate variations in EEG data into different nodes. Check the function. If this function is too simplistic or if the thresholds for mapping to different nodes are not set appropriately, it might not be sensitive enough to the nuances in the data. Let's explain it with GitHub co-pilot, such as what to do to say the function is responsible for conveying EEG, EEG data actually begins by normalizing power, function maps each normalized power to musical nodes using map power node. Mapping is done within the specific range of nodes for each type of brain wave. For example, delta waves are mapped to nodes between C3 and E3. Yes, that's normalization, it's not great enough, is it? Yes, if we are always getting C3, F3, B3, E4, it's always in the low side of the range, so there's not enough variability. To examine the logic of your map EEG data to nodes function, let's break down its current implementation and consider potential adjustments for better responsiveness to the EEG data. Normalize EEG data. It normalizes the EEG data for each frequency band, delta, theta, alpha, beta, using the normalized power function. Map normalized values to nodes, it then maps these normalized values to a predefined range of nodes. Normalized power to node mapping, how does the function decide which node to assign for a given normalized value? This mapping is crucial. If the mapping is not sensitive enough to the changes in normalized values, they might always yield the same nodes. Range of nodes, the current mapping uses a limited range, C3, 2, E3 feet for delta, F3, 2, A3 feet for theta, and so on. If the normalized values don't vary much or if the range is too narrow, this could lead to consistently mapping to the same nodes. Distribution of normalized values, check how the normalized values are distributed. If most values are clustered around certain points, EEG, always close to zero or one, this could lead to similar nodes being chosen each time. Okay, let's do those one by one. Enhanced sensitivity, adjust the mapping logic to be more sensitive to the changes in normalized values. This might involve using a more granular set of nodes or a different method for mapping values to nodes. Yes, on LinkedIn there was a suggestion to use like a table, a lookup table. I assume we could try that, check the node again. Yeah, so the suggestion was to have certain EEG markets trigger wave tables tried as a prompt in a second, let's just go with what it already gave us. Widen the node range, consider using a broader range of nodes for each EEG frequency band to capture more variability. Non-linear mapping, if the relationship between EEG data and musical nodes is not linear, explore non-linear mapping techniques. For instance, certain ranges of EEG data could map to different octaves or scales. What we're really doing this, what we're really doing a look, a rhythmic finding log, or not that type of log, should be math log-ish scale, it's not cool. I'm pretty sure we had something, so we're not looking at the Python script to the one we're trying to do as much as possible in JavaScript. Pretty sure we had the, yeah, for the worry, have a non-linear mapping. Non-linear mapping, if the relationship between EEG data and musical nodes is not linear, explore non-linear mapping techniques. For instance, certain ranges of EEG data could map to different octaves or scales. And the normalized powers, we're really doing that, I don't know what you're talking about. Statistical features, instead of using raw or normalized power values directly, consider mapping based on statistical features of the EEG data, like variance, peaks, or average power over a period. Map power to node function should take a normalized power value and map it to a musical node within a specified range. The function needs to determine which node corresponds to the given power level based on the range you provide. Here's a basic implementation of such a function. Musical scale definition, the nodes array defines one octave of a chromatic scale. Node index calculation, the get node index function calculates a global index for a given node by considering its octave. This makes it easier to work with a continuous range of nodes across octaves. Mapping normalized power, the function maps the normalized power, a value between zero and one, to an index between the start and end nodes indices. Calculating node to an octave, it then determines the corresponding node to an octave from the calculated index. Returning the node, finally, the function returns the node name combined with its octave. This function can be used to map a power value after normalization for node. For example, if the normalized power value is 0.5, and the range is from C3 to C4, it will return a node halfway in this range. Make sure to adjust the node range and the scale as per your requirements. This one would not be using get node range to get node index. Every time you ask it something, it's having another function. Node index calculation, the get node index function calculates a global index for a given node by considering its octave. This makes it easier to work with a continuous range of nodes across octaves. Do we have to have another function? Can we just modify the existing ones? Passing integer. Already have it then get node range to enhance sensitivity. Something went wrong. Enhancing the sensitivity of your map EEG data to nodes function involves making the node selection more responsive to the subtle changes in the EEG data. This can be achieved by refining the way you process the EEG data and map it to musical nodes. Here are some strategies to consider. Adjust normalization parameters. Fine tune the min power and max power values in your normalized power function to better reflect the range of your EEG data. This ensures that the normalization process captures more subtle variations. Use statistical measures. Instead of normalizing raw power values, consider using statistical measures like variance, standard deviation, or mean of the power values over a window of time. Broader node range. Use a wider range of nodes for each EEG frequency band. This allows for more nuance mapping of EEG data to nodes. Dynamic node selection. Instead of mapping EEG bands to fixed node ranges, dynamically select the node range based on the characteristics of the EEG data, e.g. higher variance in the data could lead to a broader range of nodes. Broader node range. Use a wider range of nodes for each EEG frequency band. This allows for more nuance mapping of EEG data to nodes. Instead of mapping EEG bands to fixed node ranges, dynamically select the node range based on the characteristics of the EEG data, e.g. higher variance in the data could lead to a broader range of nodes. Use nonlinear functions or lookup tables that can translate EEG data variations into a wider variety of nodes. This might involve more complex algorithms that can better capture the essence of the EEG data. Utilize multiple features of the EEG data, like amplitude, frequency content, and temporal changes. Influence different musical elements, such as pitch, volume, and rhythm. Add console.log statements to monitor how changes in EEG data influence the normalized values and the resulting nodes. This will help you understand whether the adjustments are effectively enhancing sensitivity. In this example, the node ranges are expanded, and nodes are selected based on the position in the range determined by the normalized power. This approach offers more sensitivity to the EEG data's subtleties. Let's try this one and get the GitHub co-pallet working as well. Generating anything, okay, now it is just a cheat, it's a common previous function out, so we're explaining the range of the nodes alpha, beta, into log, map nodes, okay, we'll do a log there, let's read this and try it out. In this example, the node ranges are expanded, and nodes are selected based on the position in the range determined by the normalized power. This approach offers more sensitivity to the EEG data's subtleties. Remember, enhancing sensitivity might require several iterations of testing and adjusting. The key is to find the right balance that accurately reflects the variability in your EEG data's musical expression. Yeah, this is pretty annoying. It's the same steel, it's 34. Yeah, just doing the first, the first nodes. There's some sort of perceived volume change, but that's actually not changing, still getting the same nodes. So we tried the increasing the range. And console.log statements in your mapping function to log the normalized EEG values in the corresponding node selections. This can help identify where the lack of variation is occurring. To effectively debug your map EEG data to nodes function and understand why it's consistently mapping to the same nodes, C3, C4, C5, C6, you can add console.log statements at strategic points. These will help you trace how the EEG data is being processed and mapped to musical nodes. Here's how you can modify your function with additional logging. EEG data variability. Check the logged EEG data for mapping to see if the input EEG data varies significantly between different readings. Normalized power values. Observe the normalized powers log. If the normalized values for different EEG bands are very similar each time, it suggests an issue with the normalization process. Note mapping output. The map.log shows the result of mapping normalized powers to nodes. If these nodes are always the same, it suggests that either the EEG data lacks variability or the mapping logic isn't sensitive enough to the changes in EEG data. If EEG data doesn't vary much, the issue might be with the data itself for how it's being preprocessed. If normalized values don't vary, you might need to adjust the normalized power function or the way you're extracting features from the EEG data. If the mapping logic always results in the same nodes for different normalized values, consider refining the map power to note function to be more responsive to the input values. By carefully analyzing the output from these logs, you should be able to pinpoint where the issue lies and make the necessary adjustments to ensure a more dynamic and responsive mapping of EEG data to musical nodes. The consistent mapping of normalized EEG power values to the same nodes, C3, C4, C5, C6, despite their variability indicates that the map power to note function may not be sensitive enough to the changes in the normalized power values. There are a few strategies you can apply to address this. The current node mapping logic seems to produce the same nodes for a range of normalized values. Consider revising this logic to be more sensitive to small changes in the normalized values. You could implement a more granular mapping strategy, perhaps using a larger set of nodes or a different method to assign nodes based on the normalized power values. Ensure that the map power to note function effectively differentiates between different ranges of power values. It might be useful to log the exact values being passed into this function and the resulting node choices to debug effectively. Consider using a nonlinear mapping method if the relationship between power values and musical nodes isn't linear. Instead of a direct mapping from power values to note names, you could use a scheme that takes into account more aspects of the EEG data or includes a random element to add variability. For example, you could map different EEG features, like the peak frequency or the presence of certain waveforms, to different musical characteristics, such as rhythm, harmony or dynamics. Add detailed console.log statements in the map power to note function to understand how the power values are being converted into nodes. Log the exact input value in the corresponding output node to see if there's a pattern or if the function isn't responding as expected to different inputs. By making these adjustments, you should be able to achieve a more dynamic mapping that accurately translates the nuances of EEG data into a varied musical output. It seems like obviously the problem is that the change in normalized powers is too small. Current change in normalized powers seem to be too small. How do we address this? Yep. The issue you're facing, where small changes in normalized powers are not sufficiently influencing the node selection, suggests that the mapping from EEG data to musical nodes needs to be more sensitive or varied. Here are a few strategies to address this. Find a node selection, expand the range of nodes used for each EEG band and made the mapping more sensitive to smaller changes in power values. For example, instead of mapping to a range of four nodes, like C3, 2, C6, consider using a larger set that includes intermediate nodes or semitones. Nonlinear scale, implement a nonlinear mapping scale where small changes in power can result in more significant changes in node selection. This can be achieved using logarithmic or exponential scales, or even custom functions that better represent the relationship you want between EEG data and nodes. Advanced data processing utilizes more sophisticated statistical methods or machine learning models that can learn to map EEG data to nodes in a way that captures more subtle variations in the data. Dynamic range mapping adjusts the map power to node function to dynamically choose the range of nodes based on the spread of the normalized power values. For instance, if the spread is narrow, you can map it to a wide range of nodes to enhance sensitivity. Utilize other EEG aspects, instead of relying solely on power values, instead of other aspects of EEG data that might offer more variability and can be mapped to different musical parameters. In this example, I use the square root scale math sqrt power for mapping, which makes the function more sensitive to smaller changes in power values. By implementing these strategies, you can make the node mapping more responsive to the subtleties in EEG data, leading to a more varied and dynamic musical output. So this function is currently not being used. Why? I think to help us tonight because it suggests that this change. In this example, I use the square root scale math sqrt power for mapping, which makes the function more sensitive to smaller changes in power values. That also means we have to revert to the previous version of map EEG data to nodes. It's not being... It seems to have gone wrong. Generate... Yes, reverting to the previous version of your map EEG data to nodes function could be a good starting point, especially if you plan to implement a new mapping strategy like a non-linear mapping approach. The idea is to use a function that can be more responsive to the subtle variations in the EEG data. Here's how you can proceed. Go back to the version of map EEG data to nodes that work best in the context of your application, or which you feel has the most potential for effective modification. Within the map EEG data to nodes function, implement the non-linear mapping logic. This could involve using a square root, logarithmic, or custom non-linear scale for mapping EEG power values to nodes. Ensure that your map power to node function can handle the non-linear mapping effectively. This function should be able to translate the scaled power values into a broader and more varied range of nodes. After making these changes, it's crucial to test the output with various EEG data samples to see how the mapping responds. Adjust the mapping parameters as needed based on results. Here's a revised example of map EEG data to nodes incorporating a non-linear mapping approach. In this function, math. SQRT is used to apply a non-linear scaling to the normalized values before they are mapped to nodes. This should make the function more sensitive to small changes in EEG data. Remember, the key to a successful mapping strategy is experimentation and adjustment based on testing and feedback. Fine-tuning the parameters and the mapping logic will be essential to achieving the desired responsiveness and variability in your musical output. We're a better deck, but the range, right, this is increasing the range as well, is it? Okay, we're doing the square root already elsewhere. We're doing it already. Can expand the range here, though. C3, C6, C3, C6, C6. Okay, so this is HL4. This is no seizure. This is yes seizure. And the nodes, yeah, almost the same. So, yeah, so the variability between the nodes is still too small, too outliking. Here, it varied quite a bit. Yeah, we have those edge problems as well. When you get to the end, just try quickly. That's a lot of noise. Maybe it sounds like music to you. I don't know. Let me know. So we're doing a non-linear mapping. I think this range is not large enough. I don't know anything about music, so it's hard for me to tell how large this range is. The range of nodes you've defined in the map power to node function calls is determined by the starting and ending nodes for each EEG frequency band. Delta, theta, alpha, beta. Let's break down each range. Delta range, C3, 2, C6, this. This range starts from C in the third octave, C3, and goes up to C in the sixth octave, C6. In terms of piano keys, this covers three full octaves, which includes 36 white keys and 25 black keys, totaling 61 keys. The range is from D in the fourth octave, D4, to D in the sixth octave, D6. This range covers two full octaves, plus one node, which includes 24 white keys and 17 black keys, totaling 41 keys. This range starts from E in the third octave, E3, and goes up to E in the sixth octave, E6. It spans three full octaves, comprising 36 white keys and 25 black keys, a total of 61 keys. Beta range, F3, 2, F6. The range is from F in the third octave, F3, to F in the sixth octave, F6. Similar to the Delta and the Alpha ranges, it covers three full octaves, which includes 36 white keys and 25 black keys, totaling 61 keys. Each of these ranges is quite extensive, allowing for a significant variation in the nodes that can be mapped from the EEG data. The broader the range, the more potential for capturing subtle differences in the EEG data through musical expression. However, it's important to ensure that the mapping logic within map power to note is sensitive enough to utilize the full breadth of these ranges effectively. Yeah, I'm pretty sure we do not have enough variability in the power, which is odd considering there is quite a bit of variability there. Yeah, I think I know what the problem might be. The power, yeah, should probably be something like 0 to 3000 instead of automated. What does that mean? Why would doing this again just place this with it rewrite the function or explain it? The normalized power function is designed to take an array of power values, presumably from EEG data, and normalize these values to a standard range, typically 0 to 1. This normalization process is crucial for ensuring that the EEG data can be consistently interpreted regardless of its original scale. Here's a typical implementation of such a function. Input validation, the function first checks whether the input is a valid array and contains elements. Finding min and max values, it calculates the minimum min power and maximum max power values in the array. Handling uniform data, if all values in the array are the same, min power equals equals equals max power. The function returns an array where all elements are set to a default normalized value, EEG 0.5. This avoids division by zero in the next step. Normalization, each element in the array is normalized to a range of 0 to 1. This is done by subtracting min power from the value and then dividing by the range, max power, min power. The normalized output is useful for further processing steps, especially when mapping EEG data to other domains like musical notes, as it ensures consistency regardless of the original amplitude of the EEG signals. Let's try this quickly. This is odd, very odd. Obviously, it didn't work because it's returning all 0.5. I was actually generating some noise. During the previous one, we'll just try something more simple, set of this to just zero, because that should be zero, to about 3,000, getting a bit more variability there. And we know that it's about four or five, let's say channel seven, so this should be seizure. This should be no seizure. The notes are actually quite different. So yeah, we're having a problem with that last value there. What does it sound like a different shell? Yeah, that seizure really sounded different. Yeah, so seizure has this lower frequencies. So that was seizure, no seizure. Yeah, I don't know if you're getting much out of it. Does it sound like music to you? Probably highly unlikely. I don't know. It's a quite subjective music perception, isn't it? Okay, that updated function that you generated did not work, it giving a lot of errors. Now the range now seemed to be better, getting a variable notes when scrolling through the EEG data. Can we make sure we have a nonlinear mapping? If you're experiencing errors with the updated normalized power function and want to ensure a nonlinear mapping to enhance variability in note selection, let's approach this step by step. First, let's revisit the normalized power function to ensure it works correctly without errors. We'll keep it simple and linear for now, as the nonlinear part will be in the mapping function. To achieve nonlinear mapping, we can modify the map power to note function. A common nonlinear approach is to use a logarithmic or exponential scale, which can make the mapping more sensitive to changes in lower power values and less sensitive at higher values, or vice versa. Here's an example using a simple exponential mapping. The transform power variable applies a nonlinear transformation to the normalized power. Here, we use a square function, math.pow, power, 2, where you can experiment with other functions like logarithmic, math.log, power plus 1, depending on the desired effect. The transform power value is then used to select a node from the node range. Once you've implemented these changes, test the system with your EEG data. Look for variability in the map notes as you scroll through the data. Adjust the nonlinear... We get it. We get it. It's already more using... Sorry, I wasn't actually listening to what it said. We'll do it again. Should it work correctly without errors? Yeah, of course. What's it suggesting? Is this another function? No, I already have that. It's suggesting changing it back to... I think this will work. Here's a quick go. Let's do a shadow 5. No, it doesn't work. Someone is asking if using chgbt useful or functional. Does it serve any purpose? Yes, it does. Not in this case though, like the last prompt for whatever reason is not generating anything useful. Revet, revet. That it's working again. We'll make any changes. Let's say... Which job do you like? Actually quite different. Do we stuff something up? Yes, we are checking that it is correct or incorrect. I mean, it's highly subjective in this case. So this is how the whole thing sounds. Now the question is what your window size is. Make it very small. Say one second. Does it sound like music? My second favorite thing is having an issue with this. Scroll is too jumpy. Scroll is too jumpy. Can quickly fix that from html scroll. Mean mox. Make the step one. Yeah, let's make it two. Sorry for that. The other thing is the window size is also the step of two. Can you hear this horrible? Like when the notes are transitioning between one another. Here the duration. So this duration default should be say 0.5 play frequency. The volume make the duration 0.1. Oops. What did we stuff up? The common stuff. Yeah, it's when the transition is not great. Transition is not great. But changing at all. Okay, let's hold the input there. Just play the frequency. Same here. And in default volume should be good. Duration 0.1. Yes, it's trying to. GPT-4 is trying to change the normalized power function. That doesn't work. Long linear mapping. And the auto note. Yes, we could use instead of square root. Can use a power. So using exponential function. Note the index when it transforms power. Should have said that this is missing. Or it doesn't. It's the explanation for it. The transform power variable applies a nonlinear transformation to the normalized power. Here we use a square function. Math.power2. That you can experiment with other functions like logarithmic. Math.log. Power plus 1. Depending on the desired effect. The transform power value is then used to select a note from the note range. It's just to work better. As the transition between notes is real bad. That's something to fix. Should just keep playing until it's changed. That's what we can try next time. Just send a prompt. For the duration of the note being played. Can we make it so it continuously plays until the scroller is changed again? So essentially when we load the page nothing happens. When we start scrolling the note is played until we move the scroller again. Yep. To achieve the behavior where a note continuously plays until the scroller is moved again. You'll need to adjust the logic around how and when sounds are played and stopped in your application. Here's a strategy to implement this. You'll need a global variable to keep track of the currently playing sound or oscillator. So you can stop it when the scroller is moved again. Modify the play frequency function to check if a sound is already playing. If so, stop it before starting a new sound. In a scroller's event handler, ensure that moving the scroller stops the currently playing sound and then starts playing the new sound corresponding to the new position. Global oscillator. Current oscillator is a global variable that keeps track of the currently playing sound. In play frequency, before starting a new sound, the existing sound is stopped if current oscillator is not null. The file scroller event listener stops the currently playing sound and starts a new one based on the scroller's position. With this setup, when you load the page, no sound will play until you start scrolling. Once you scroll, the sound for the corresponding position will start playing and will continue to play until you scroll again, at which point the sound will change. It sounds good, but why do I have a feeling this chord would not work? It looks like your message might have been cut off. Could you please provide more details or clarify your questions so I can assist you better? I can't actually say anything. Which stack we are using? What are we using? Flask. It's a Flask application. Hopefully it will soon be available on binarykills.com. Still in development, but we, as you can tell, it's doing something kinda useful, which is loading this EEG that has a seizure in it, epileptic seizure. This is pure EEG, like the highest quality you can get. It's from an implant, from 16 implanted cells, implanted electrodes sitting on top of the brain, eight on the left, eight on the right. Now we wanna, this tool allows you to do the noising and other things, but as another feature, we wanted to play sound, which it does. We haven't published it yet, so when you load the page, by the way, it won't show you the whole spectrum, because we're essentially turning power in each frequency band, delta, theta, alpha, beta, into a corresponding musical note. So you select your channel, scroll through the stuff, and then write in the comments if this sounds like music to you, or if you like it or not. Well, if you don't like it, you have to make a suggestion. Let's do how to improve it, can change your window size and the like. Now, next time we are working this tomorrow, we'll be trying to actually make it play continuously, until you move the scroller again. So loading the page doesn't play anything. We're actually not allowed the browser one. Let us play anything and select your channel. As you start playing, you wanna make it so it keeps playing until you move the scroller again, and then we'll play the new four notes. We have an event, obviously, for the scroller input. Over here, yes, that's the same, and then we're changing the play frequency function. Get rid of that. This function, we still have volume and duration as an input. Why? Global oscillator. Current oscillator is a global variable that keeps track of the currently playing sound. In play frequency, before starting a new sound, the existing sound is stopped if current oscillator is not null. The file scroller event listener stops the currently playing sound and starts a new one based on the scroller's position. With this setup, when you load the page, no sound will play until you start scrolling. Once you scroll, the sound for the corresponding position will start playing and will continue to play until you scroll again, at which point the sound will change. Okay, we'll continue this next time. Bye for now.