Body: <p>I have frequency EEG data from fall and non-fall events and I am trying to incorporate it with accelerometer data that was collected at the same time.  One approach is, of course, to use two separate algorithms and find the threshold for each.  Then comparing the threshold of each.  In other words, if the accelerometer algorithm predicts a fall (fall detected = 1) and the EEG algorithm detects a fall, based on the power spectrum (fall detected = 1), then the system outputs a "1" that a fall was truly detected.  This approach uses the idea of a simple AND gate between the two algorithms.  </p>
  
  <p>I would like to know how to correctly process the data so that I can feed both types of data into one algorithm, perhaps a CNN. Any advice is really appreciated, even a lead to some literature, articles or information would be great.  </p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-16T01:40:03.233Z
Comments.Text: Hi and welcome to this community! Why do you want to compare the EEG and the accelerometer data? What your main goal or problem? What do you mean by compare? Furthermore, it may be useful if you describe more in detail your data. For example, the number of examples, etc.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-16T22:45:50.440Z
Comments.Text: @GeorgeWhite Thanks for trying to edit this post in order to improve it (specifically, to integrate the information given in the answer), but I've already done it (in case you didn't notice it), so I had to reject your edit. However, I really appreciate that you spent some time to improve this site :)
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-16T23:03:18.583Z
Comments.Text: I must have been doing at about the same time. Thanks for straightening it out.
Comments.UserDisplayName: George White
Tags: <neural-networks><deep-learning><comparison><datasets><computational-linguistics>
Title: How can I compare EEG data with accelerometer data in 1 algorithm?
UserDisplayName: sam
ThreadText: # How can I compare EEG data with accelerometer data in 1 algorithm?
  
  (Asked by: username_0 on 2020-01-15)
  
  I have frequency EEG data from fall and non-fall events and I am trying to incorporate it with accelerometer data that was collected at the same time. One approach is, of course, to use two separate algorithms and find the threshold for each. Then comparing the threshold of each. In other words, if the accelerometer algorithm predicts a fall (fall detected = 1) and the EEG algorithm detects a fall, based on the power spectrum (fall detected = 1), then the system outputs a "1" that a fall was truly detected. This approach uses the idea of a simple AND gate between the two algorithms.
  
  I would like to know how to correctly process the data so that I can feed both types of data into one algorithm, perhaps a CNN. Any advice is really appreciated, even a lead to some literature, articles or information would be great.
  
  **Tags:** neural-networks, deep-learning, comparison, datasets, computational-linguistics
  > **Comments:**
  > **username_1**: Hi and welcome to this community! Why do you want to compare the EEG and the accelerometer data? What your main goal or problem? What do you mean by compare? Furthermore, it may be useful if you describe more in detail your data. For example, the number of examples, etc.  
  > **username_1**: @GeorgeWhite Thanks for trying to edit this post in order to improve it (specifically, to integrate the information given in the answer), but I've already done it (in case you didn't notice it), so I had to reject your edit. However, I really appreciate that you spent some time to improve this site :)  
  > **username_2**: I must have been doing at about the same time. Thanks for straightening it out.  
  
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  ## 0 answers
  
  