Merge branch 'master' of github.com:Mentalab-hub/explorepy into develop

examples/acquisition-example.py

@@ -15,6 +15,14 @@ def my_exg_function(packet):
     #############
 
 
+def my_env_function(packet):
+    """A function that receives env packets(temperature, light, battery) and does some operations on the data"""
+    print("Received an environment packet: ", packet)
+    #############
+    # YOUR CODE #
+    #############
+
+
 def my_orn_function(packet):
     """A function that receives orientation packets and does some operations on the data"""
     timestamp, orn_data = packet.get_data()
@@ -38,6 +46,7 @@ def main():
     # Subscribe your function to the stream publisher
     exp_device.stream_processor.subscribe(callback=my_exg_function, topic=TOPICS.raw_ExG)
     exp_device.stream_processor.subscribe(callback=my_orn_function, topic=TOPICS.raw_orn)
+    exp_device.stream_processor.subscribe(callback=my_env_function, topic=TOPICS.env)
     try:
         while True:
             time.sleep(.5)

examples/band_power_plot.py

@@ -0,0 +1,108 @@
+import time
+from collections import deque
+
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.animation import FuncAnimation
+
+import explorepy
+from explorepy.packet import EEG
+from explorepy.stream_processor import TOPICS
+
+rows, cols = 8, 1024
+data_buf = deque([deque(maxlen=cols) for _ in range(rows)])
+for row in data_buf:
+    row.extend(range(1024))
+
+
+def get_data_buf():
+    output = np.array([[0] * 1024 for _ in range(rows)])
+    for i in range(len(output)):
+        output[i] = np.array(data_buf[i])
+    return output
+
+
+def on_exg_received(packet: EEG):
+    _, data = packet.get_data()
+    for r in range(len(data)):
+        for column in range(len(data[r])):
+            data_buf[r].append(data[r][column])
+
+
+exp_device = explorepy.Explore()
+# Subscribe your function to the stream publisher
+
+exp_device.connect(device_name="Explore_AAAK")
+exp_device.stream_processor.subscribe(callback=on_exg_received, topic=TOPICS.raw_ExG)
+
+# Define the frequency bands
+bands = {
+    'Delta': (0.5, 4),
+    'Theta': (4, 8),
+    'Alpha': (8, 13),
+    'Beta': (13, 30),
+    'Gamma': (30, 50)
+}
+
+# FFT and signal parameters
+sampling_rate = 250  # in Hz
+num_channels = 8
+num_samples = 1024
+
+# Set up the figure and axes for subplots
+fig, axs = plt.subplots(4, 2, figsize=(10, 15), sharex=True)
+fig.tight_layout(pad=4.0)  # Adjust layout padding
+
+# Flatten the 2D array of axes for easier iteration
+axs = axs.flatten()
+
+# Initialize bar plots for each subplot
+bars = []
+for ax in axs:
+    bars.append(ax.bar(bands.keys(), [0] * len(bands), color='skyblue'))
+    ax.set_ylim(0, 1)  # Set an initial y-limit, can adjust based on your data
+    ax.set_xlabel('Frequency Band')
+    ax.set_ylabel('Power')
+
+
+def update(frame):
+    eeg_signals = get_data_buf()  # Generate data for all channels
+
+    for idx, (axis, bar) in enumerate(zip(axs, bars)):
+        eeg_signal = eeg_signals[idx]
+
+        # Perform FFT
+        fft_values = np.fft.fft(eeg_signal)
+        fft_frequencies = np.fft.fftfreq(num_samples, 1 / sampling_rate)
+        fft_magnitude = np.abs(fft_values) ** 2  # Power spectrum
+
+        # Only consider positive frequencies
+        positive_frequencies = fft_frequencies[:num_samples // 2]
+        positive_magnitude = fft_magnitude[:num_samples // 2]
+
+        # Calculate band powers
+        band_powers = []
+        for band, (low, high) in bands.items():
+            indices = np.where((positive_frequencies >= low) & (positive_frequencies <= high))
+            band_power = np.sum(positive_magnitude[indices])
+            band_powers.append(band_power)
+
+        # Update the bar heights
+        for b, power in zip(bar, band_powers):
+            b.set_height(power)
+
+        # Optionally adjust y-axis limits based on data
+        axis.set_ylim(0, max(band_powers) * 1.1)
+        axis.set_title(f'Channel {idx + 1}')
+
+    return [b for bar in bars for b in bar]  # Flatten the list of bars
+
+
+# Create the animation object
+ani = FuncAnimation(fig, update, interval=500, blit=True)  # Update every 500 ms (0.5 seconds)
+
+# Display the plot
+plt.show()
+
+while True:
+    time.sleep(1)

examples/eegsynth_demo/buffer.ini

@@ -0,0 +1,10 @@
+[general]
+debug=2
+delay=0.010
+
+[redis]
+hostname=localhost
+port=6379
+
+[fieldtrip]
+port=1972,1973,1974

examples/eegsynth_demo/historycontrol.ini

@@ -0,0 +1,17 @@
+[general]
+debug=2
+
+[redis]
+hostname=localhost
+port=6379
+
+[history]
+window=10
+; window length for smoothing (s)
+stepsize=0.05
+; update time (s)
+
+[input]
+; control values to plot, separated by comma
+freeze=0
+channels=spectral.channel1.alpha,spectral.channel2.theta,spectral.channel2.beta

examples/eegsynth_demo/lsl2ft.ini

@@ -0,0 +1,16 @@
+[general]
+debug=1
+
+[fieldtrip]
+hostname=localhost
+port=1972
+
+[redis]
+hostname=localhost
+port=6379
+
+[lsl]
+; this can be used to select the desired stream (in case there are multiple)
+name=
+type=ExG
+timeout=30  ; in seconds

examples/eegsynth_demo/outputmidi_loopback.ini

@@ -0,0 +1,33 @@
+[general]
+debug=1
+delay=0.05
+monophonic=1 ; boolean
+
+[redis]
+hostname=localhost
+port=6379
+
+[midi]
+device=IAC Driver Bus 1
+channel=1
+
+[control]
+; you can specify different MIDI message types here: controlXXX, noteXXX, polytouchXXX, aftertouch, pitchwheel, start, continue, stop, reset, note
+
+[trigger]
+; you can specify different MIDI message types here: controlXXX, noteXXX, polytouchXXX, aftertouch, pitchwheel, start, continue, stop, reset, note
+note=post.channel1.alpha
+start=post.channel1.alpha
+
+[duration]
+note=0.5      ; the note will be switched off after the specified time (in seconds)
+
+[velocity]
+
+[scale]
+; scale and offset can be used to map Redis values to MIDI values between 0 to 127
+; the default scale for all channels is 127
+
+[offset]
+; the default offset for all channels is 0
+note=40

examples/eegsynth_demo/outputmidi_loopback_2.ini

@@ -0,0 +1,33 @@
+[general]
+debug=1
+delay=0.05
+monophonic=1 ; boolean
+
+[redis]
+hostname=localhost
+port=6379
+
+[midi]
+device=IAC Driver Bus 2
+channel=1
+
+[control]
+; you can specify different MIDI message types here: controlXXX, noteXXX, polytouchXXX, aftertouch, pitchwheel, start, continue, stop, reset, note
+
+[trigger]
+; you can specify different MIDI message types here: controlXXX, noteXXX, polytouchXXX, aftertouch, pitchwheel, start, continue, stop, reset, note
+note=post.channel2.thetabetaratio
+start=post.channel2.thetabetaratio
+
+[duration]
+note=0.5      ; the note will be switched off after the specified time (in seconds)
+
+[velocity]
+
+[scale]
+; scale and offset can be used to map Redis values to MIDI values between 0 to 127
+; the default scale for all channels is 127
+
+[offset]
+; the default offset for all channels is 0
+note=40

examples/eegsynth_demo/postprocessing.ini

@@ -0,0 +1,23 @@
+[general]
+delay=0.05
+debug=2
+
+[redis]
+hostname=localhost
+port=6379
+
+[initial]
+; here you can specify the initial values of some control values
+
+[input]
+; the keys here can have an arbitrary name, but should map those in the output section
+; the keys must be lower-case. values should not contain an equation, only one-to-one mappings
+alpha_1=spectral.channel1.alpha
+theta_2=spectral.channel2.theta
+beta_2=spectral.channel2.beta
+
+[output]
+; besides +, -, /, *, the equations also support log, log2, log10, exp, power from numpy
+; and compress, limit, rescale, normalizerange, normalizestandard from EEGsynth
+post.channel1.alpha = limit(alpha_1 / 1750, 0, 0.8)
+post.channel2.thetabetaratio = limit(theta_2/beta_2 / 6, 0, 0.9)

examples/eegsynth_demo/preprocessing.ini

@@ -0,0 +1,36 @@
+[general]
+delay=0.10
+debug=1
+
+[redis]
+hostname=localhost
+port=6379
+
+[input_fieldtrip]
+hostname=localhost
+port=1972
+timeout=30
+
+[output_fieldtrip]
+hostname=localhost
+port=1973
+
+[processing]
+window=0.12
+;smoothing=0.2
+reference=none
+lowpassfilter=45
+highpassfilter=2
+filterorder=511
+;downsample=1
+notchfilter=50
+
+[scale]
+highpassfilter=1
+lowpassfilter=1
+notchfilter=1
+
+[offset]
+highpassfilter=0
+lowpassfilter=0
+notchfilter=0

examples/eegsynth_demo/push2lsl.py

@@ -0,0 +1,5 @@
+import explorepy
+
+expdev = explorepy.Explore()
+expdev.connect("Explore_AAAI")
+expdev.push2lsl()

examples/eegsynth_demo/spectral.ini

@@ -0,0 +1,47 @@
+[general]
+debug=2
+delay=0.1
+
+[redis]
+hostname=localhost
+port=6379
+
+[fieldtrip]
+hostname=localhost
+port=1973
+timeout=30
+
+[input]
+; this specifies the channels from the FieldTrip buffer
+; the channel names (on the left) can be specified as you like
+channel1=1
+channel2=2
+; channel3=3
+; channel4=4
+; channel5=5
+; channel6=6
+; channel7=7
+; channel8=8
+
+[processing]
+; the sliding window is specified in seconds
+window=5.0
+
+[scale]
+window=1
+
+[offset]
+window=0
+
+[band]
+; the frequency bands can be specified as you like, but must be all lower-case
+; you should give the lower and upper range of each band
+delta=2-5
+theta=5-8
+alpha=8-12
+beta=12-30
+gamma=35-45
+
+[output]
+; the results will be written to Redis as "spectral.channel1.alpha" etc.
+prefix=spectral

examples/ssvep_demo/ssvep.py

@@ -4,6 +4,7 @@
 """
 import time
 from threading import Lock
+from psychopy_visionscience.radial import RadialStim
 from psychopy import visual, event
 import numpy as np
 from analysis import CCAAnalysis
@@ -29,9 +30,9 @@ def __init__(self, window, size, position, n_frame, log_time=False):
         pattern = np.ones((4, 4))
         pattern[::2, ::2] *= -1
         pattern[1::2, 1::2] *= -1
-        self._stim1 = visual.RadialStim(win=self._window, tex=pattern, pos=position,
+        self._stim1 = RadialStim(win=self._window, tex=pattern, pos=position,
                                         size=size, radialCycles=1, texRes=256, opacity=1)
-        self._stim2 = visual.RadialStim(win=self._window, tex=pattern*-1, pos=position,
+        self._stim2 = RadialStim(win=self._window, tex=pattern*-1, pos=position,
                                         size=size, radialCycles=1, texRes=256, opacity=1)
         self._toggle_flag = False
         self.log_time = log_time