Spaces:

MJ
/

EEG_cls

Sleeping

EEG_cls / misc.py

Mohammad Javad Darvishi

'plot using mne-python'

f462298 8 months ago

No virus

4.63 kB


	import mne
	import streamlit as st
	import matplotlib.pyplot as plt

	from braindecode import EEGClassifier
	from braindecode.models import Deep4Net,ShallowFBCSPNet,EEGNetv4, TCN
	from braindecode.training.losses import CroppedLoss

	import torch
	import numpy as np

	def set_button_state(output,col):
	# Generate a random output value of 0 or 1
	# output = 2023 #random.randint(0, 1)

	# Store the output value in session state
	st.session_state.output = output

	# Define the button color and text based on the output value
	if st.session_state.output == 0:
	button_color = "green"
	button_text = "Normal"
	elif st.session_state.output == 1:
	button_color = "red"
	button_text = "Abnormal"
	# elif st.session_state.output == 3:
	# button_color = "yellow"
	# button_text = "Waiting"
	else:
	button_color = "gray"
	button_text = "Unknown"

	# Create a custom HTML button with CSS styling
	col.markdown(f"""
	<style>
	.custom-button {{
	background-color: {button_color};
	color: black;
	padding: 10px 20px;
	border: none;
	border-radius: 5px;
	cursor: pointer;
	}}
	</style>
	<button class="custom-button">Output: {button_text}</button>
	""", unsafe_allow_html=True)


	def predict(raw,clf):
	x = np.expand_dims(raw.get_data()[:21, :6000], axis=0)
	output = clf.predict(x)
	return output


	def build_model(model_name, n_classes, n_chans, input_window_samples, drop_prob=0.5, lr=0.01):#, weight_decay, batch_size, n_epochs, wandb_run, checkpoint, optimizer__param_groups, window_train_set, window_val):
	n_start_chans = 25
	final_conv_length = 1
	n_chan_factor = 2
	stride_before_pool = True
	# input_window_samples =6000
	model = Deep4Net(
	n_chans, n_classes,
	n_filters_time=n_start_chans,
	n_filters_spat=n_start_chans,
	input_window_samples=input_window_samples,
	n_filters_2=int(n_start_chans * n_chan_factor),
	n_filters_3=int(n_start_chans * (n_chan_factor ** 2.0)),
	n_filters_4=int(n_start_chans * (n_chan_factor ** 3.0)),
	final_conv_length=final_conv_length,
	stride_before_pool=stride_before_pool,
	drop_prob=drop_prob)

	clf = EEGClassifier(
	model,
	cropped=True,
	criterion=CroppedLoss,
	# criterion=CroppedLoss_sd,
	criterion__loss_function=torch.nn.functional.nll_loss,
	optimizer=torch.optim.AdamW,
	optimizer__lr=lr,
	iterator_train__shuffle=False,
	# iterator_train__sampler = ImbalancedDatasetSampler(window_train_set, labels=window_train_set.get_metadata().target),
	# batch_size=batch_size,
	callbacks=[
	# EarlyStopping(patience=5),
	# StochasticWeightAveraging(swa_utils, swa_start=1, verbose=1, swa_lr=lr),
	# "accuracy", "balanced_accuracy","f1",("lr_scheduler", LRScheduler('CosineAnnealingLR', T_max=n_epochs - 1)),
	# checkpoint,
	], #"accuracy",
	# device='cuda'
	)
	clf.initialize()
	pt_path = './Deep4Net_trained_tuh_scaling_wN_WAug_DefArgs_index8_number2700_state_dict_100.pt'
	clf.load_params(f_params=pt_path)

	return clf


	def preprocessing_and_plotting(raw):
	fig = raw.plot(duration=10, scalings='auto',remove_dc=True,show_scrollbars=False) #, n_channels=10
	st.pyplot(fig)

	# # Plot the power spectrum
	# fig, ax = plt.subplots()
	# raw.plot_psd(fmin=1, fmax=60, ax=ax)
	# st.pyplot(fig)

	# # Plot the spectrogram
	# fig, ax = plt.subplots()
	# raw.plot_spectrogram(n_fft=512, ax=ax)
	# st.pyplot(fig)

	# # Select the first channel
	# channel = raw.ch_names[0]
	# st.write(f"Selected channel: {channel}")

	# # Plot the first channel
	# fig, ax = plt.subplots()
	# ax.plot(raw.times, raw[channel][0].T)
	# ax.set_xlabel("Time (s)")
	# ax.set_ylabel("Amplitude (µV)")
	# ax.set_title(f"EEG signal of {channel}")
	# st.pyplot(fig)

	def read_file(edf_file):
	# To read file as bytes:
	bytes_data = edf_file.getvalue()
	# Open a file named "output.bin" in the current directory in write binary mode
	with open('edf_file.edf', "wb") as f:
	# Write the bytes data to the file
	f.write(bytes_data)

	raw = mne.io.read_raw_edf('edf_file.edf')
	st.write(f"Loaded {edf_file.name} with {raw.info['nchan']} channels")
	return raw