Biological signals are analogues and hence continuous. Early EEG systems simply recorded the analogue signals and displayed it as a continuous signal graphically as wiggly lines written by little pens on a roll of paper (Fig. 1). Only after analogue-to-digital converters became available, could EEG signals and the likes be digitally sampled.
Fig. 1. ...
you can convert your data frame to numpy array using
If your data is single trial meanining two dimensional dataset, you can use
sfreq = 1000 # in Hertz
montage = 'standard_1005'
info = mne.create_info(channel_names, sfreq, channel_types, montage)
raw = mne.io....
For EEG, mostly diminishing returns. Because the contacts themselves are relatively far from the neurons generating those signals, there is a lot of correlated signal on adjacent channels. Statistical methods like clustering are often used to treat signals of interest coming from multiple channels as just a single signal. Increasing the spatial sampling ...
The key term that will help you look for research about this is the word "biomarker".
There are certainly correlations with autism and some of the measurement techniques, but they aren't specific enough to be used for diagnosis.
Plitt et al 2015 compared fMRI classification to a behavioral scale in high-functioning ASD vs IQ-matched typically ...
Not sure, but I think you'd find it interesting that there are some humans out there who do not have an intuitive sense of any number past two. For example, people who are part of hunter gatherer tribes who, for example, only need to know that they have a lot of berries, not that they have exactly 7 or exactly 65; for their lifestyle, there is no need for ...
To add to the answer provided by @AliceD, pure digital waveforms are square waveforms as they represent steps between 1s and 0s and are therefore not continuous.
Analogue waveforms are not. They are smooth continuous waves and can represent many voltage points at each millisecond, microsecond or nanosecond between the peaks and troughs.
Outputs from ADCs (...
I am unsure to what your paradigm exactly does, but as to your frequency bands a layman's interpretation would be as shown in Table 1. Hence, the frequency bands you investigate could simply be different, because the states associated with them are different.
As to your band filter - the best thing to do is find the rock stars in your field and copy their ...
The basic answer is that it depends on:
training protocol (duration, frequency)
modality (hearing, seeing, memorizing, attention, etc)
individual variance (observable changes varies between individuals - some may not respond to the training)
So it has to be determined individually for each task (not enough information provided in the original question). In ...
It is possible to look at within-subject interactions using cluster permutation tests.
I.e. Eric Maris here: https://mailman.science.ru.nl/pipermail/fieldtrip/2011-January/003447.html
2x2x2 with factors A,B,C has 4 interactions AxB, AxC, BxC and AxBxC and 8 cells
| C = 0 | B = 0 | B = 1 |
You have one p-values for each ROI timecourse (for the largest cluster) indicating whether your conditions are exchangeable. If you want to correct for the 4 ROIs you could use any adequate multiple comparison correction, from classical Bonferroni (which might be too conservative as the tests are likely not completly independent) to Benjamini-Hochberg style ...
Thank you for your interesting question!
Generally, there are three types of methods to process your EEG time-series data:
Time domain methods (e.g., regression, statistical analysis on your EEG time-series data, etc.)
Frequency domain methods (e.g., Fourier Transform)
Time-frequency domain methods (e.g., Short-Time Fourier Transform, Wavelet Transform, ...
For the sake of completeness:
eegkit, see https://cran.r-project.org/web/packages/eegkit/index.html
For "historical purposes" perhaps the following could also be of interest, although development seems somewhat stagnant lately: https://rdrr.io/cran/eegAnalysis/