 The provided JavaScript code appears to be for a web application named IEEG underscore spectrum underscore 16 underscore Chan underscore denoise underscore music. This app seems to process electroencephalography, EEG, data to create a visual and auditory experience. Key features of the code include initialization, the app initializes when the DOM content is fully loaded. It sets up various UI elements like sliders, selectors, and toggles. Data handling and rendering, the main functionality revolves around fetching EEG data based on user inputs, like channel selection, window size, etc., and rendering this data visually using plotly, a popular graphing library. Audio processing, the app also includes an audio context for playing sounds. It maps EEG data to musical notes, which are then played back as sound frequencies. User interactions, several event listeners are attached to UI elements, enabling dynamic updating of the displayed data and sound output based on user interactions. Wavled denoising, there's an option for wavled denoising, suggesting the app applies wavled transforms for signal processing. Spectrum analysis, the app appears to display both raw EEG signals and their frequency spectrums, giving users insights into the spectral characteristics of the EEG data. Data endpoints, different endpoints are used for fetching data depending on whether the app is in a production environment or running locally. Error handling, the code includes basic error handling, especially for fetching data and audio processing. SNR and MSC outputs, signal to noise ratio, SNR, and mean squared error, MSC, are calculated and displayed, indicating some form of signal quality or denoising effectiveness measurement. The code is structured to handle user inputs, process EEG data, and provide an interactive visual and auditory representation of this data. Yay, so we have this JavaScript that we were working on for a while now. We actually came to a point where we started from scratch using a code for a different application. So now it's working okay, it's 226 lines of code. I actually removed all the empty lines and comments from it, because that's essentially what the GPT-4 was just explaining. This code would potentially have more questions about it later. I just saw that GPT has the whole context. We also have this Flask application, Python code. Some of the functions are not being used. I don't know why we can remove any spaces and comments from it. We only have the comment at the top. I'll pop it into GPT-4 as well. Let's see what it says, and we run it as well. Currently running locally, plan to deploy it sometime very soon. This is what the front page looks like. It does been explained before. There are remnants of previous applications that are already deployed. This one, the new feature is that it's also converting this AEG into sound. That's how it sounds like when you scroll through this file, channel 7. This is the whole segment as a seizure in it on different channels. This channel 14 sounds more interesting. It needs to be made wider, trying to select 10 moving trouble. Let's keep it 14. It shouldn't matter much. That's what the seizure sounded like. You can try a different window in 5 seconds. It can hear the difference between seizure and no seizure. But yes, it's not great. We'll be trying to improve it in the future. It's the description of the Python code. Data with a focus on noise reduction and spectrum analysis. Key components of the script include. Flask setup, the application is built using a Flask framework, a popular choice for web applications in Python. Data loading and preparation, the app loads EEG data from a text file and prepares it for processing. This includes setting parameters like sampling frequency, start time, and duration. But with bandpass filter, functions are defined for creating and applying a but with bandpass filter, which is used to isolate specific frequency bands in EEG data. Endpoint definitions. The root endpoint serves the main HTML page. Get underscore data, endpoint handles requests for EEG data processing and visualization. It takes various parameters from the request, such as the time window, EEG channel, and whether to apply waver denoising. Waver denoising, the app optionally applies waver denoising to the EEG data, which is a method for reducing noise in signals. Spectrum analysis, the app performs spectrum analysis on the EEG data, both for the whole spectrum and specific frequency bands, Delta, Theta, Alpha, Beta, using the Welch method. Signal visualization, the processed EEG data, both original and denoised, is prepared for visualization, including a computation of signal to noise ratio, SNR, and mean squared error, MSE. JSON response, the endpoint returns a JSON object containing the processed data, including raw signals, spectrums, SNR, and MSE, which can be used for rendering visualisations on the client side. Main execution, the script runs the Flask app in debug mode when executed directly. This application appears to be a sophisticated tool for analysing and visualising EEG data, where focus on noise reduction and frequency analysis. The integration with a web interface suggests it's designed for interactive use, allowing users to select different parameters and view the corresponding EEG data and its analysis. Yes, one of the problems, for example, of future improvement, is the fact that the wavelet denoising doesn't actually make much difference. It doesn't make a difference at all. In terms of the sound, it makes a little bit of difference. You can actually see the difference over there. Yeah, this is two of them, overlaid on top of one another. You can see it even more. And how different wavelets are doing. That's what it's giving in the RNY. It seems like bi-autogonal 1.1 makes the larger difference. Yeah, if the training is off, the filtering doesn't really work. There's also this crackling noise when nodes are changing. It's only happening when scrolling. We actually tried addressing it yesterday. So, I'm wondering now if Python was the last code that was uploaded to ChBD. If I ask any questions now, it will try and address them through Python code instead of JavaScript. I don't know. There is a bit of crackling noise when nodes are changing. So, when scrolling through the EEG file, there's some sort of sound interference. It seems to be between the different nodes. The crackling noise you're experiencing when scrolling through the EEG file and triggering different nodes could be due to several factors related to audio processing in your application. There are also some potential causes and solutions. Audio buffering issues. If the audio buffers are not being handled smoothly when switching between nodes, it can result in a crackling sound. Ensure that the transitions between audio buffers are seamless. Yeah, I think we checked it yesterday. It wasn't the buffering, but something else. Actually, now, I don't know if you can hear it, but it's doing some sort of decay. So, it's not just turning the node off when it's playing, but there should be a function for it. There should be some sort of delay. Why don't I see that code? It's actually unusual. It should be changing play frequency. This function. Yeah, this ramp. It's called ramp. Right, yeah, this duration. Increase that. It's not working as expected. Making a difference. You need to see for any magic numbers. This duration is longer. Yeah, there is this crackling sound between when scrolling. I think channel 14 or something, that's something more interesting. I don't know if you can hear it as well, but I want to get rid of that crackling sound between nodes. What could it be? Audio buffering issue. Ensure that the transitions between audio buffers are seamless. Rapid frequency changes. Sudden changes in frequency can cause a crackling noise. Implementing a smooth transition or a fade-in, fade-out effect between nodes can help. Overlapping nodes. If multiple nodes are being played simultaneously or if a new node starts before the previous one has completely finished, it can cause interference. Implement a mechanism to stop the previous node before starting a new one. Volume peaks. High volume levels or sudden volume changes can lead to distortion. Apply normalization or dynamic range compression to manage volume levels. Waveform discontinuities. If the waveform of your audio signal has discontinuities, sharp edges, it can produce a crackling sound. Using waveforms with smooth transitions like sine waves can mitigate this. Yeah, we are using four A sine waves playing at the same time. This tells you how the mapping is actually happening. Nodes to play can go. It's normalize power. It's the normalize power range, which should be from 0 to 1, should it? Based on the code, what normalize power range should be. Based on the JavaScript code you provided earlier, the normalize power range for each EEG frequency band, Delta, Theta, Alpha, Beta, appears to be between 0 and 1. This is inferred from the normalize power function in the code. Here's a brief breakdown of how the normalization is done. Yes, it was normally way too small. Power array aggregation. The function takes an array of power values for a specific EEG frequency band and computes the average power. Look it over here. Control of 5. Yeah, that's the theme. The first, this power value is way too small. Should have had the amplitudes as well. Power array aggregation. The function takes an array of power values for a specific EEG frequency band and computes the average power. Clamping. The aggregated power is then clamped between a minimum, min power, and a maximum, max power value. In your code, min power is set to 0, and max power is set to 3,000. Normalization. The clamped power value is normalized to a range of 0 to 1 using a formula. This normalized power is then used for further processing, such as mapping to musical notes. The normalization process ensures that the power values are scaled to a consistent range, which is important for comparing values across different segments of EEG data or different EEG channels. Numbers seem to be too small. These values fall within the expected range of 0 to 1, as per the normalization process described in your code. The normalization ensures that the power levels of different EEG frequency bands are scaled to uniform range, making them comparable and suitable for further analysis or mapping to other modalities, like musical notes in your application. The values indicate the relative power within each frequency band as a fraction of the defined maximum power, which was 3,000 in your code. Lower values suggest lower power within that specific frequency band. For instance, the delta band has the highest normalized power among the four, though it's still quite low in absolute terms, suggesting that the overall power in the EEG signal for this segment is relatively low across all bands. We're at just the maximum power to 2,000 instead of 3,000. So yeah, this normalized power for a delta band is higher. It's going up to 15K. With this number, it's very low to 12K. Alright, so now it's more sensitive and these numbers go, you get some larger closer to 1 values as well. Let me know if it's better or not. It essentially became way more sensitive. It actually shows you the exact mapping. Reducing a maximum power from 3,000 to what will this do? Reducing the maximum power in the normalization process from 3,000 to 100 will have a significant impact on the normalized power values of your EEG frequency bands. Here's what you can expect. Higher normalized values, since the denominator in your normalization formula becomes smaller, max power, min power, the resulting normalized values will be higher for the same raw power measurements. This is because you're compressing the range of possible raw power values into a smaller scale. Increased sensitivity to changes, with a lower maximum power value, small changes in the raw power will result in more significant changes in the normalized power. This could make the system more sensitive to minor variations in the EEG signal. I was saying, sorry, my microphone was muted, but I was saying it's not clipping, so it could make the system more sensitive to minor variations, potential clipping. Potential for clipping. If the raw power values exceed the new maximum power, 100 in this case, they will be clipped to the maximum during a clumping step. This means all power values above 100 will be treated as 100 in the normalization process, potentially losing some granularity in the higher range of the power spectrum. Right, so it's not clipping in this particular case, but yes, it might clip with other signals. I don't know if this power goes higher than the current at 20K. Impact on further processing. Since the normalized power values are used for further processing, like mapping to musical notes, you can expect a more pronounced and varied response in this subsequent processing. For instance, if you're using these values to control aspects of music or visualization, the changes might become more dramatic or noticeable. Right, yeah, they're definitely more noticeable. Adjusting for realistic EEG power levels. If the actual power levels of your EEG data are generally much lower than 3000, reducing the maximum power to a more realistic value like 100 might provide a normalization that is more representative of the actual power distribution in the EEG signal. Using the maximum power to a more realistic value like 100 might provide a normalization that is more representative of the actual power distribution. Yes, so this power distribution, the normalized power distribution is more realistic, yes. In summary, reducing the maximum power value in the normalization process will make the system more responsive to changes in EEG power levels, but it may also increase the risk of clipping for higher power values. This adjustment should be made carefully considering the typical power range of your EEG data and the desired sensitivity of your application. So that's what a seizure sounds like on channel 15, with a clip over there, you know, with more power in there. I was trying to see what the fact window size has. Why is it only two powers? Ah, no, it is clipping. Right, so it is clipping on channel 14. That's the problem. Yes, when the mapping power is one, that means the normalized power is clipping. I don't know how bad is it because it's still playing note. Let's just double check it should be playing the highest note using index 36, playing SC6, check the code, using index 36, SC6. Yes, so it's playing the highest note in the delta range. It should be also playing the highest note in theta range. In alpha range, the mapping power, the normalized power is 0.57, which translates into G5. So G5 should be somewhere in the middle of between E3 and E6, I think, just assume that is correct. Anyway, reducing this max power, essentially a magic number because it's not a maximum power, it's the maximum normalized power anyway, but that's resulting in normalized numbers between 0 and 1. So we had a description of that already, I think yesterday, try copilot to explain this. So for each type of primary band, a frequency band within the EG, the normalized power is passed to the map power to note function, the normalized power of delta waves is mapped to note between C3 to C6, that's correct. Inside the map power to note function, the get note range function is called to get an array of all the notes. Get note range, start note and note, start this again. I have channel 7 as default, it's not the most interesting one. Music wise, 15 sounds a bit better. Let's try 30 and we haven't tried 30. So window size obviously matters a lot. Let's try 5, 6. You can also use your keyboards. The other thing there's an obvious overlap. Yeah, the overlap quite a bit, that's the other problem. Yeah, we actually should have something that changes by how much the scroll is moving. It should depend on the window size. We have the standard 8, 10 seconds of a cross. Anyway, you can use your keyboard, mouse, there's an overlap in the scrolling. It's kind of obvious we can change that. So on one hand we want to keep as many things as default as possible. On the other hand we want to give you more control over how this works. So let me know what you think. Essentially all these variables could be made unified. Well, they're not constant, but they are. Well, I call them magic numbers, but let's see if it understands. Magic numbers in programming is a numeric value that is used in a code without any explanation of what it represents. That's right. Here's the magic numbers in the provided code. File scroller. Yes, this file scroller is important. We have the volume, the volume seem to be okay. Well, yeah, that's the one I can let you adjust, like the user to adjust, but that's probably a bad idea. Duration, yeah, we could have a slider for the duration as well. I know the 10 is passing into base 10. So now that is explainable. That's not magic. Volume, duration is the magic numbers. We can just let the user control those. Yes, 1000 if it's converting from seconds to milliseconds. It's not a magic number as well, because we know why it's 1000. Yeah, those are magic numbers. Zero, four, mean and max, normalized power. Minimum maximum power values used for normalization. The 100 there is a magic number. That will be harder to explain. Why is it 100? Is that 11? Start node index. This is the index of the last node in the nodes array. Why? Because there's 11 of those. I don't know why there's 11 of those. Yeah, we did explain this yesterday. Well, GPT-4 explained yesterday. I didn't quite understand it. There's a 49. It's a constant using a formal to convert a node to a frequency. These three and one looks like magic numbers to me. That seemed to just work. Yeah, we might add the volume and duration. At this stage. That's how all channels sound like. I don't think we can look at channel one as well. It's not clipping. Channel one. If it sounds any better or worse. We also ideally have a selection of the musical instrument. Eventually we want to extract some other features, not just a power in each frequency band and map them into pitch and timbre. How is mapping EEG to music currently happening? I don't know if this is a generic response. Certainly. Let's look at both the JavaScript and Python Flask code and suggest some improvements for each. Optimize performance for large datasets. If the EEG data can be large, instead of optimizing how the data is processed and rendered. For example, use web workers for data processing to avoid blocking the main thread. Refactor repeated code. The fetch data and render function contains repeated blocks of code for setting up clock configurations. This can be refactored into a separate function to improve readability and maintainability. Improve error handling. Add more robust error handling, especially for network requests and data processing. Display user-friendly error messages in the UI if something goes wrong. UI responsiveness. Ensure that the UI remains responsive when data is being fetched or processed. Indicators like loading spinners can be helpful. Audio processing. Investigate and address the crackling noise issue during note transitions. Consider using a library like Tone. JS for more sophisticated audio processing capabilities. Code documentation. Add comments to complex sections of the code to make it easier for others or yourself in the future to understand the logic and flow. Code organization. Separate the data processing logic from the Flask Roots. This can involve creating a dedicated module or class for handling EEG data processing. Error handling. Improve error handling in the Flask Roots. Catch potential exceptions, especially when dealing with file operations or external libraries. Input validation. There is a more comprehensive validation for the query parameters in get underscore data root. Ensure that the inputs are within expected ranges and types. Optimize data loading. Loading large data files can be resource-intensive. Consider using a more efficient data storage format like HDF5 or implement caching mechanisms to reduce load times. Use of global variables. The data variable is loaded globally. This can be problematic for larger datasets and can affect scalability. Consider loading data on demand or using a database. Restful API design. Make sure your API follows restful principles. This includes using proper HTTP methods, status codes, and structuring endpoints logically. Unit tests. Add unit tests for data processing functions. This ensures that any changes in the code do not break existing functionality. Configuration management. Move configuration settings, like FS, start underscore time underscore in underscore seconds, etc. Separate configuration file or use environment variables. This makes it easier to modify settings without changing the code. Logging. Implement logging to capture important events, errors, and debugging information. This will be invaluable for maintenance and troubleshooting. Both sets of code could benefit greatly from these improvements, enhancing performance, maintainability, and user experience. Certainly. Matic numbers in programming refer to numbers that appear in the code without clear explanation, so you can start to understand and maintain. Here's a list of magic numbers from the provided JavaScript and Python Flask code. 10 in const second value equals pass int, file scroller, value, 10, base for passing string to integer. 0.5 in gain node. Gain, set value at time, volume, audio context, current time, initial gain value for the audio node. 0.03 in play frequency, frequency, volume equals 0.03, duration equals 1.2, default volume level for playing a frequency. 1.2 in play frequency, frequency, volume equals 0.03, duration equals 1.2, the default duration for playing a frequency. 0.2 in gain node. Gain, linear ramp to value at time, 0, audio context, current time plus duration 0.2, time before the end of the duration to start ramping down the volume. 1000 in set timeout equals greater than oscillator, stock, duration 1000, millisecond conversion factor. 2 in multiplier equals 2 used in the wavelet denoising section. 0.5, 4, 8, 13, 30 in the band underscore range's definition, specific frequency values for EEG bands. 400 in FS equals 400 sampling frequency. 31,610 in start underscore time underscore in underscore seconds equals 31,610 start time for data loading in seconds. 110 in duration underscore to underscore load underscore in underscore seconds equals 100 in duration underscore to underscore display underscore initially underscore in underscore seconds equals 10 durations for data loading in initial display. 5 in defbutter underscore band pass, low cut, high cut, FS, order equals 5, default order for the butworth filter. 0.5 in nyq equals 0.5 asterisk FS, used to calculate the Nyquist frequency. 2 in multiplier equals 2 within the get underscore data route for wavelet denoising. 3000 in the normalization process, maximum power value for normalization. 0.5, 4, 8, 13, 30 in the band underscore range's definition, specific frequency values for EEG bands. In both cases, replacing these magic numbers with well-named constants or configuration parameters would enhance the readability and maintainability of the code. For example, you might replace 400 with sampling underscore frequency or 10 with default underscore window or duration to make the codes in 10 clearer. If I continue this next time, let me know if there's any relevant questions. Hopefully that tool will become available on binarykills.com this week or next week. And I'll see you later.