def connection(self):
""" This function use MulesClient class to connect to the Mules server. Mules server will provides
the eeg signal recorded from epoc sensors. The function also start a connection with an Unity
server application. """
## 1) Connection with Mules and retrieve EEG data parameters
# Creates mules_client object
if hasattr(self, 'mules_client'):
print('Connection already established')
else:
self.mules_client = mules.MulesClient(
'localhost', 30000) # connects with MuLES at 127.0.0.1 : 30000
# Retrieve recording information
self.device_name = self.mules_client.getdevicename() # get device name
self.channel_names = self.mules_client.getnames() # get channel names
self.fs = 1.0 * self.mules_client.getfs() # get sampling frequency
print('fs= ', self.fs)
self.nb_chl = len(self.channel_names) # Number of channel
self.data = np.zeros(
(int(self.bufsize * self.fs), self.nb_chl)) # init buffer
# TCP Client for Unity
self.unity = TcpClient('localhost', 40000)
self.unity.connect()
"""
import mules
import numpy as np
import bci_workshop_tools as BCIw
import matplotlib.pyplot as plt
if __name__ == "__main__":
# MuLES connection paramters
mules_ip = '127.0.0.1'
mules_port = 30000
# Creates a mules_client
mules_client = mules.MulesClient(mules_ip, mules_port)
# Device parameters
params = mules_client.getparams()
#%% Set the experiment parameters
training_secs = 20
win_test_secs = 1 # Length of the Test Window in seconds
overlap_secs = 0.7 # Overlap between two consecutive Test Windows
shift_secs = win_test_secs - overlap_secs
eeg_buffer_secs = 30 # Size of the EEG data buffer (duration of Testing section)
#%% Record training data
# Record data for mental activity 0
BCIw.beep()
Uses the Sound-playing interface for Windows to play a beep
Arguments
f: Frequency of the beep in Hz
d: Duration of the beep in ms
"""
winsound.Beep(f,d)
if __name__ == "__main__":
plt.close('all')
# 1. Acquisition is started
# creates mules_client object and:
mules_client = mules.MulesClient('127.0.0.1', 30000) # connects with MuLES at 127.0.0.1 : 30000
device_name = mules_client.getdevicename() # get device name
channel_names = mules_client.getnames() # get channel names
fs = 1.0 * mules_client.getfs() # get sampling frequency
# 2. Defining EEG data buffer for 10 seconds
n_samples = 10 * fs;
eeg_data_buffer = np.zeros((n_samples, len(channel_names)), float)
time_vector = np.arange(0 , n_samples) / fs
# 3. Flush old data from the Server
mules_client.flushdata()
beep(600,250)
# Create Figure
channel = 4
