You can use EEGLab for processing of your data

The tutorials of EEGLab could be found at https:/eeglab.org/tutorials/. Re-referencing, band-pass filtering, and ICA-based artifacts removing are necessary steps to preprocess EEG. Besides, deep learning models are commonly designed by using Pytoch.

reprocessing EEG data in CSV format typically involves several steps, such as data cleaning, filtering, and artifact removal. Here's a general outline of the preprocessing steps you can perform on EEG data in CSV format:

import pandas as pd # Load the EEG data from CSV eeg_data = pd.read_csv('eeg_data.csv')

from scipy.signal import butter, filtfilt # Butterworth bandpass filter function def butter_bandpass(lowcut, highcut, fs, order=5): nyquist = 0.5 * fs low = lowcut / nyquist high = highcut / nyquist b, a = butter(order, [low, high], btype='band') return b, a # Apply a bandpass filter to the EEG data def apply_bandpass_filter(data, lowcut, highcut, fs, order=5): b, a = butter_bandpass(lowcut, highcut, fs, order=order) filtered_data = filtfilt(b, a, data) return filtered_data # Define the cutoff frequencies for the bandpass filter lowcut = 1.0 # Lower cutoff frequency in Hz highcut = 30.0 # Upper cutoff frequency in Hz # Define the sampling frequency (adjust according to your data) fs = 250 # Sampling frequency in Hz # Apply the bandpass filter to the EEG data filtered_data = apply_bandpass_filter(eeg_data.values, lowcut, highcut, fs)

These are the general steps involved in preprocessing EEG data in CSV format. However, the specific preprocessing steps may vary depending on your research question, the characteristics of the EEG data, and the analysis techniques you plan to use. It's always important to consider the specific requirements of your study when preprocessing EEG data.