To process and analyze EMG (Electromyography) data using MATLAB, you would typically follow these steps: data import, filtering, RMS conversion, Fourier Transform, and frequency analysis. It seems you can already convert your EDF files to .txt format. After you have your data in a .txt format, you can import it into MATLAB.

Here is a simplified example of how you can accomplish this:

%% 1. Load data from .txt file

% Replace 'filename.txt' with your actual filename

emgData = load('filename.txt');

%% 2. Filter the data

% Design a bandpass filter that passes frequencies between 20 Hz and 500 Hz

% You may want to adjust these values depending on your specific EMG data.

Fs = 1000; % sampling frequency (adjust based on your actual sampling rate)

Fnyquist = Fs/2; % Nyquist frequency

lowCut = 20/Fnyquist; % normalized low cutoff frequency

highCut = 500/Fnyquist; % normalized high cutoff frequency

[b,a] = butter(2, [lowCut highCut], 'bandpass'); % 2nd order Butterworth filter

filteredEMG = filtfilt(b, a, emgData);

%% 3. Convert to RMS

window = 50; % 50 samples moving window for RMS calculation

rmsEMG = sqrt(movmean(filteredEMG .^ 2, window));

%% 4. Fast Fourier Transform

% Compute the FFT of the filtered EMG signal

n = length(filteredEMG); % Length of the signal

f = Fs*(0:(n/2))/n; % Frequency vector

Y = fft(filteredEMG);

P = abs(Y/n); % Normalized FFT (single-sided spectrum)

%% 5. Mean Frequency

% Compute the mean frequency of the spectral power

P = P(1:n/2+1);

P(2:end-1) = 2*P(2:end-1); % Double the amplitude of frequencies excluding DC and Nyquist

meanFreq = sum(P.*f')/sum(P); % Mean frequency calculation

This script provides an overview of the tasks you mentioned. You'll need to adapt it to your needs and ensure the parameters used (like the sampling frequency, filter design, RMS window size) are appropriate for your data. You also mentioned you want to select certain intervals and analyze those - this would require additional code to allow for user input and selection.

You can use the plot function to plot and visualize the data at any stage. For instance, plot(emgData) or plot(filteredEMG).

For the interactive selection of data intervals, you can use the 'ginput' function or 'imrect' function (if you have the Image Processing Toolbox). However, designing such an interactive script may require a more advanced understanding of MATLAB scripting. If you are new to MATLAB, I recommend checking out the official MATLAB documentation and tutorials to get a feel for the MATLAB coding environment. You might also find the MATLAB user community to be a helpful resource.

Please note that processing EMG signals can be complex and may require a good understanding of signal processing and the physiological characteristics of EMG. Always ensure your processing steps are appropriate for your specific application and consult an expert if needed.

Thank you very much for the answer Joshua Depiver! I shall try to use the scripts you sent me.

To accept input data from a text file in MATLAB, you can use the `importdata` function. This function reads data from a file and returns a data structure containing the imported data.

Here's an example code snippet that reads in data from a text file:

```matlab

% Read in data from a text file

data = importdata('data.txt');

% Extract the data and time vectors from the data structure

time = data(:, 1);

signal = data(:, 2);

```

In this example, we use the `importdata` function to read in data from a text file named `data.txt`. We then extract the time and signal vectors from the resulting data structure.

To filter the data, you can use MATLAB's built-in filtering functions, such as `filter` or `medfilt1`. For example, to apply a low-pass filter to the signal, you can use the following code:

```matlab

% Design a low-pass filter

fc = 1000; % cutoff frequency

fs = 20000; % sampling frequency

[b, a] = butter(2, fc/(fs/2));

% Filter the signal

filtered_signal = filter(b, a, signal);

```

In this example, we design a second-order Butterworth low-pass filter with a cutoff frequency of 1000 Hz. We then filter the signal using the `filter` function with the filter coefficients `b` and `a`.

To convert the filtered signal to RMS, you can use the `rms` function. For example:

```matlab

% Convert the filtered signal to RMS

rms_signal = rms(filtered_signal);

```

In this example, we use the `rms` function to compute the RMS value of the filtered signal.

To compute the Fast Fourier Transform (FFT) of the RMS signal, you can use the `fft` function. For example:

```matlab

% Compute the FFT of the RMS signal

nfft = 2^nextpow2(length(rms_signal));

fft_signal = fft(rms_signal, nfft);

```

In this example, we compute the FFT of the RMS signal using the `fft` function. We first compute the number of points for the FFT using `nextpow2`, which returns the next power of 2 greater than or equal to the length of the signal. We then compute the FFT using `fft`.

To get the mean frequency of spectral power from certain 50ms intervals, you can use the `pwelch` function. This function computes the power spectral density (PSD) estimate of a signal using Welch's method, which divides the signal into overlapping segments and computes the PSD of each segment.

Here's an example code snippet that computes the mean frequency of spectral power from 50ms intervals:

```matlab

% Compute the PSD of the RMS signal using Welch's method

window_size = 0.05; % 50ms window size

noverlap = round(window_size/2); % 50% overlap

nfft = 2^nextpow2(window_size*fs); % FFT size

[Pxx, f] = pwelch(rms_signal, window_size*fs, noverlap*fs, nfft, fs);

% Compute the mean frequency of spectral power from 50ms intervals

power_threshold = 0.1; % power threshold

mean_freq = mean(f(Pxx > power_threshold));

```

In this example, we compute the PSD estimate of the RMS signal using Welch's method with a window size of 50ms, 50% overlap, and an FFT size calculated using `nextpow2`. We then compute the mean frequency of spectral power from 50ms intervals with a power greater than a threshold of 0.1.

Code snippets are just examples, and the specific details of your signal processing pipeline may vary depending on the characteristics of your data and the requirements of your application.

Hope it helps!!

Credit: mainly AI tools