I need help regarding the analysis of EEG continuous data. Briefly, the study aims to explore the relationship between brain processes and performance on a neuropsychological test and if this test is able to detect differences in participants’ cognitive status. The test includes 21 stimuli (e.g everyday documents) and 42 related questions.
Can anyone help me clarify which steps and statistical analysis should I use? After studying the relevant litterarure, I concluded that synchronisation analysis suits very well with the study’s aims. It will help clarify which pairs of electrods were activated as well as the relative energy of such activity during the compeltion of the whole task and during the completion of each item. If this is correct, then what should be the next step?
I think, the next step is to check which electrods pairs contibute the most to total scores and scores on each item. Then, to check if there are differences in synchronisation for right and wrong responses. So, is my thinking correct? and if so what statistical analysis should I use?
Thank you very much in advance.
What signal processing and programming environment are you using? Those will have some influence on your choice of algorithms. This doesn’t directly answer your question, but EEGLAB / BCILAB and OpenViBE might prove as useful guides as you build your analysis.
What types of documents did you display as stimuli? What are some examples of the related question/stimulus pairs?
It sounds like you might have a labeled data set, e.g. whether or not questions were answered correctly, which could open up possibilities for supervised machine learning.
Thank you very much for your response. Regarding the first question (signal processing and programming environmennt), I really do not know! I am preparing a proposal, as part of my application of a Master’s programm.
Regarding your second question, stimuli are documents such as food and medecin labels. There are two questions to each stimuli and multiple choice answers. The point is to use EEG during task and investigate which brain processes are used in the excecution of the test (as a whole) and of each question (or item, I haven’t decided yet). I also want to check if there are differerences in EEG between participants who scored high and low in task. I hope I gave you a better picture. Any advice?
It sounds like you might be analyzing the EEG data from at least two perspectives
- sub-samples of the EEG, I.e. epochs, for each question
- average overall activation across the entire recording
The individual question epochs could be labeled as “correct” or “incorrect”, based on recorded response, and used as part of a supervised machine learning analysis.
The “average overall activation patterns” could be used in, for example, unsupervised cluster analysis or a regression based on percentage of correct responses.
Those are just some ideas based on my very limited experience, so take them with a grain of salt.
I highly recommend the lecture series Introduction to Modern Brain-Computer Interface Design as a reference/overview of many related concepts, such as signal processing and machine learning.
Many thanks Brylie! Your ideas are very helpful.