diff --git a/docs/logo.jpg b/docs/logo.jpg
new file mode 100644
index 00000000..f301ba4f
Binary files /dev/null and b/docs/logo.jpg differ
diff --git a/examples/acquisition-example.py b/examples/acquisition-example.py
index 21c18a39..5b3cdfd9 100644
--- a/examples/acquisition-example.py
+++ b/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)
diff --git a/examples/band_power_plot.py b/examples/band_power_plot.py
new file mode 100644
index 00000000..0ffab75d
--- /dev/null
+++ b/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)
diff --git a/examples/eegsynth_demo/MentalabEEGSynth.vcv b/examples/eegsynth_demo/MentalabEEGSynth.vcv
new file mode 100644
index 00000000..4d143abd
Binary files /dev/null and b/examples/eegsynth_demo/MentalabEEGSynth.vcv differ
diff --git a/examples/eegsynth_demo/buffer.ini b/examples/eegsynth_demo/buffer.ini
new file mode 100644
index 00000000..aa13d865
--- /dev/null
+++ b/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
\ No newline at end of file
diff --git a/examples/eegsynth_demo/historycontrol.ini b/examples/eegsynth_demo/historycontrol.ini
new file mode 100644
index 00000000..62221209
--- /dev/null
+++ b/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
\ No newline at end of file
diff --git a/examples/eegsynth_demo/lsl2ft.ini b/examples/eegsynth_demo/lsl2ft.ini
new file mode 100644
index 00000000..919687ab
--- /dev/null
+++ b/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
diff --git a/examples/eegsynth_demo/outputmidi_loopback.ini b/examples/eegsynth_demo/outputmidi_loopback.ini
new file mode 100644
index 00000000..225a9500
--- /dev/null
+++ b/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
\ No newline at end of file
diff --git a/examples/eegsynth_demo/outputmidi_loopback_2.ini b/examples/eegsynth_demo/outputmidi_loopback_2.ini
new file mode 100644
index 00000000..d140b0df
--- /dev/null
+++ b/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
diff --git a/examples/eegsynth_demo/postprocessing.ini b/examples/eegsynth_demo/postprocessing.ini
new file mode 100644
index 00000000..2d471dc9
--- /dev/null
+++ b/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)
\ No newline at end of file
diff --git a/examples/eegsynth_demo/preprocessing.ini b/examples/eegsynth_demo/preprocessing.ini
new file mode 100644
index 00000000..d62453bf
--- /dev/null
+++ b/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
\ No newline at end of file
diff --git a/examples/eegsynth_demo/push2lsl.py b/examples/eegsynth_demo/push2lsl.py
new file mode 100644
index 00000000..41fd6f44
--- /dev/null
+++ b/examples/eegsynth_demo/push2lsl.py
@@ -0,0 +1,5 @@
+import explorepy
+
+expdev = explorepy.Explore()
+expdev.connect("Explore_AAAI")
+expdev.push2lsl()
diff --git a/examples/eegsynth_demo/spectral.ini b/examples/eegsynth_demo/spectral.ini
new file mode 100644
index 00000000..26be9855
--- /dev/null
+++ b/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
\ No newline at end of file
diff --git a/examples/ssvep_demo/ssvep.py b/examples/ssvep_demo/ssvep.py
index 637081c0..0ef11ef9 100644
--- a/examples/ssvep_demo/ssvep.py
+++ b/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
diff --git a/examples/wiki-ecg-analysis.py b/examples/wiki-ecg-analysis.py
new file mode 100644
index 00000000..b109101d
--- /dev/null
+++ b/examples/wiki-ecg-analysis.py
@@ -0,0 +1,46 @@
+import neurokit2 as nk
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import pyedflib
+
+def edf_to_arr(edf_path):
+    f = pyedflib.EdfReader(edf_path)
+    n = f.signals_in_file
+    signal_labels = f.getSignalLabels()
+    sigbufs = np.zeros((n, f.getNSamples()[0]))
+    for i in np.arange(n):
+        sigbufs[i, :] = f.readSignal(i)
+    
+    return sigbufs
+
+data = edf_to_arr("../data/wiki-ECG-resting_ExG.bdf") # read in bdf file
+data = data[1] # channel 1 holds ECG data
+data *= 1e-3 # scale uV to mV for ECG analysis in neurokit
+ecg_signals, info = nk.ecg_process(data[5000:25000], sampling_rate=250) # convert 
+
+rpeaks = info["ECG_R_Peaks"]
+cleaned_ecg = ecg_signals["ECG_Clean"]
+
+intervalrelated = nk.ecg_intervalrelated(ecg_signals)
+intervalrelated.iloc[0,1:83]
+
+nk.ecg_plot(ecg_signals, info)
+fig = plt.gcf()
+fig.set_size_inches(20, 12, forward=True)
+fig.savefig("../plots/resting_ecg.png")
+
+ecg_signals, info = nk.ecg_process(data[18000:20000] , sampling_rate=250) # take a subset from the middle of recording and apply neurokit
+rpeaks = info["ECG_R_Peaks"]
+cleaned_ecg = ecg_signals["ECG_Clean"]
+plot = nk.events_plot(rpeaks, cleaned_ecg[0:cleaned_ecg.shape[0]])
+fig = plt.gcf()
+fig.set_size_inches(20, 12, forward=True)
+fig.savefig("../plots/resting_rpeaks.png")
+
+peaks, info = nk.ecg_peaks(data[5000:25000], sampling_rate=250)
+hrv_time = nk.hrv_time(peaks, sampling_rate=250, show=True)
+hrv_time
+fig = plt.gcf()
+fig.set_size_inches(20, 12, forward=True)
+fig.savefig("../plots/sv_ecg_time.png")
\ No newline at end of file
diff --git a/examples/wiki-sleep-analysis.py b/examples/wiki-sleep-analysis.py
new file mode 100644
index 00000000..04b286a0
--- /dev/null
+++ b/examples/wiki-sleep-analysis.py
@@ -0,0 +1,61 @@
+# Data processing
+## Load packages
+import mne
+import yasa
+import numpy as np
+import matplotlib.pyplot as plt
+
+## Load data
+ch_names = ['EOG-above', 'EOG-canthus', 'EMG-chin', 'Dry-O2', 'Dry-C2', 'Dry-F2', 'Sticky-C1', 'Wet-Fc1']
+data = np.loadtxt("wiki-sleep-analysis-data/Mentalab-sleep-analysis_ExG.csv", skiprows=1, delimiter=',').transpose()[1:9]
+sf = 250
+ch_types = ["eog", "eog", "emg", "eeg", "eeg", "eeg", "eeg", "eeg"]
+info = mne.create_info(ch_names = ch_names, sfreq = sf, ch_types = ch_types)
+raw = mne.io.RawArray(data, info)
+raw.apply_function(lambda x: x * 1e-6) # scale muV to V
+
+## Pre-processing
+raw.filter(0.1, 40) # Apply a bandpass filter from 0.1 to 40 Hz
+
+# Detecting sleep stages
+sls = yasa.SleepStaging(raw, eeg_name="Sticky-C1")
+
+## Computing predicted labels
+y_pred = sls.predict()
+y_pred[0:40]
+
+## Computing the hypnogram
+hypno = yasa.Hypnogram(y_pred)
+hypno_int = yasa.hypno_str_to_int(y_pred)
+hypno_up = yasa.hypno_upsample_to_data(hypno=hypno_int, sf_hypno=(1/30), data=data, sf_data=sf)
+
+# Inspecting bandpower
+data_filt = raw.get_data() * 1e6
+data_c1_uV = data_filt[6]
+bandpower_stages = yasa.bandpower(data_c1_uV, sf = 100, win_sec=4, relative=True, hypno=hypno_up, include=(0, 1, 2, 3, 4))
+bandpower_avg = bandpower_stages.groupby('Stage')[['Delta', 'Theta', 'Alpha', 'Sigma', 'Beta', 'Gamma']].mean()
+bandpower_avg.index = ['Wake', 'N1', 'N2', 'N3', 'REM']
+
+print(bandpower_avg)
+
+# Spectrogram and hypnogram
+print(hypno_up.shape, 'Unique values =', np.unique(hypno_up))
+fig = yasa.plot_spectrogram(data_c1_uV, sf, hypno=hypno_up, fmax=30, cmap='Spectral_r', trimperc=5)
+fig.show()
+
+# Slow wave and sleep spindle detection
+## Slow waves
+sw = yasa.sw_detect(data_c1_uV, sf, hypno=hypno_up)
+sw.summary()
+
+sw.plot_average(time_before=0.4, time_after=0.8, center="NegPeak");
+plt.legend(['C1'])
+plt.show()
+
+## Sleep spindles
+sp = yasa.spindles_detect(data_c1_uV, sf)
+sp.summary()
+
+sp.plot_average(time_before=0.6, time_after=0.6);
+plt.legend(['C1'])
+plt.show()
\ No newline at end of file
diff --git a/src/explorepy/_exceptions.py b/src/explorepy/_exceptions.py
index 2d8a0cff..9fdefdea 100644
--- a/src/explorepy/_exceptions.py
+++ b/src/explorepy/_exceptions.py
@@ -40,14 +40,12 @@ class ReconnectionFlowError(Exception):
     """
     pass
 
-
 class BleDisconnectionError(Exception):
     """
     Reconnection flow error, only thrown when device is reconnecting
     """
     pass
 
-
 if sys.platform == "darwin":
     class BluetoothError(Exception):
         """
diff --git a/src/explorepy/explore.py b/src/explorepy/explore.py
index 8757820d..41d11f9c 100644
--- a/src/explorepy/explore.py
+++ b/src/explorepy/explore.py
@@ -19,6 +19,7 @@
 import time
 from threading import Timer
 
+import explorepy
 import numpy as np
 from appdirs import user_cache_dir
 
diff --git a/src/explorepy/exploresdk.py b/src/explorepy/exploresdk.py
index 5451bf50..e13c341c 100644
--- a/src/explorepy/exploresdk.py
+++ b/src/explorepy/exploresdk.py
@@ -5,8 +5,6 @@
 # the SWIG interface file instead.
 
 from sys import version_info as _swig_python_version_info
-
-
 if _swig_python_version_info < (2, 7, 0):
     raise RuntimeError("Python 2.7 or later required")
 
@@ -64,8 +62,6 @@ class _SwigNonDynamicMeta(type):
 
 
 import collections.abc
-
-
 class SwigPyIterator(object):
     thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
 
diff --git a/src/explorepy/parser.py b/src/explorepy/parser.py
index 4fcda800..48d6a3c2 100644
--- a/src/explorepy/parser.py
+++ b/src/explorepy/parser.py
@@ -262,7 +262,6 @@ def _parse_packet(self, pid, timestamp, bin_data):
             raise FletcherError
         return packet
 
-
 class FileHandler:
     """Binary file handler"""