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    Thread: Chat gtp rem diction ideas using Lora scripts.

    1. #1
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      Chat gtp rem diction ideas using Lora scripts.

      "Here is an example of how LoRA scripts could be used for REM detection via eye tracking:

      Begin by collecting a large dataset of eye movement data from individuals in various stages of sleep along with a full eeg for each person involved with the dataset.
      Preprocess the data to remove any artifacts and filter out unwanted noise.

      Use the preprocessed data to train an AI model to recognize patterns of eye movement that correspond to REM sleep. when 25% of eeg detect rem with corresponding rem videos 25% threshold, all the way up to 100% when both video of eye movements and egg are at 100% rem is almost a given. The final product shuld include both the eeg readout and rem videos from the dataset. So rem detection with eye tracking will be accurate.

      Fine-tune the model using LoRA scripts to improve its accuracy and performance.
      Deploy the model in real-time using eye-tracking devices to detect when an individual enters REM sleep. Remember the model needs to associate both the eeg readout from the dataset along with video from the dataset, so real time rem detection with eye tracking only can be accurate. Integrate the REM detection system into a sleep monitoring platform to provide users with information about their sleep patterns and overall sleep quality.

      This is just one example of how LoRA scripts could be used in the context of REM detection via eye tracking. The specific implementation would depend on the details of the dataset, the eye-tracking hardware being used, and other factors.".

      I was messing around with Chat gpt, and thought I'd ask questions about Lora scripts for rem detection by using a dataset. I'm not 100% an expert but unlike the units years ago that tried to do rem detection with poor eeg readouts on the end user. This idea chat gtp gave seems quite brilliant to me: train the ai with a full high end eeg on a large dataset along with all the associated eye movements from the same dataset then assign a percentage of rem diction from similar eye movements to eeg until the eye movements and eeg are at 100%. It seems quite a good idea to me. Anyway anyone else think what chat gtp wrote made any sense? Or is this just nonsense?
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      As far as I understand it, it kind of makes sense to me. I wonder if you would need a very large data set if the program was just for one person. I also wonder if you need an AI program for it at all, or if the difference between REM and non-REM is obvious. I have been toying with the idea of making something like this, with the hopes of making a cheap and easy REM detection device that you don't need to wear while asleep.
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      Very cool idea! Are you using local ai's using WebUI such as alcapa or Vicuna? Or OpenAi GPT? *just curious*
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      "Very cool idea! Are you using local ai's using WebUI such as alcapa or Vicuna? Or OpenAi GPT? *just curious*"

      No, I'm not actually doing it, or have the resources, just putting the idea out there for anyone willing to try.
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      Hey I work with building machine learning models and this is a great idea and it's definitely possible to apply some version of this.

      The way I see it there are two approaches how this could work. With one you would use an large language model like you're suggesting. In that instance I would say to either finetune one of the many existing open source models on the EEG REM data or train a LoRA if not finetune. But like on the numbers, I would not go for multi-modality here - stick with tokens.

      And then the other option which could also work really well would be to create a image classifier on the graphing data! You would create a number of classes of states in the form of images. So one class is 'this graphing image means they're almost certainly in REM!" and one class can say 'this person is currently experiencing deep sleep' and so on. And then train the model on those classees. Then when it comes to inferencing in real time you would have the model continually read the current state of the graph for whoever is sleeping and it will constantly spit out it's confidence every moment of the state of each class.

      For example you could get as many read outs a second of something like:

      Patient's current state (confidence):

      Deep sleep (87%)
      REM sleep (02%)
      etc for how many classes you want to track.

      I have the hardware and experience to build such systems, especially image classifiers. I don't know much about EEG machines and whatnot though!

      Just want to add that I looked into this more. I found a device called the Muse S which is a headband that can be worn at night and it tracks EEG and heart rate data and more. It does not have any dreaming functionality, but it does had an SDK (software development kit)! Which would give a developer access to read all of the live data and develop custom applications. It has Bluetooth connection which is ideal as no cables would have to be hooked up while sleeping.

      I submitted a request for access to the SDK. I'm going to look into if the data coming out of this is reliable and how good the REM detection really is. If that looks good and my SDK request is approved I'm going to get one and build a model to try out your idea pointofbeing.
      Last edited by Lang; 01-30-2024 at 06:29 PM. Reason: Merge post

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