diff --git a/.gitignore b/.gitignore
index 04ada82..7aa907c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,3 +16,5 @@ __pycache__/
 .cache
 .vs
 env/
+
+muselsl/docs
diff --git a/README.md b/README.md
index caccf1c..8461167 100644
--- a/README.md
+++ b/README.md
@@ -1,98 +1,46 @@
 # Muse LSL
 
-This is a collection of Python scripts to use the Muse 2016 BLE headset with LSL.
+This is a Python package for streaming and visualizing EEG data from the Muse 2016 headset.
 
 ![Blinks](blinks.png)
 
 ## Requirements
 
-The code relies on [pygatt](https://github.com/peplin/pygatt) for the BLE communication.
-pygatt works on Linux and should work on Windows and macOS provided that you have a BLED112 dongle.
-You have to use the development version of pygatt, that can be installed with pip using:
+The code relies on [pygatt](https://github.com/peplin/pygatt) for the BLE communication. pygatt works on Linux and should work on Windows and macOS provided that you have a BLED112 Bluetooth dongle.
 
-`pip install git+https://github.com/peplin/pygatt`
+*Note: Another option for connecting to a Muse on Windows is via [BlueMuse](https://github.com/kowalej/BlueMuse/tree/master/Dist) which will output the same LSL stream format as muse-lsl.
 
-Note: Another option for connecting to Muse on Windows is [BlueMuse](https://github.com/kowalej/BlueMuse/tree/master/Dist)
+You will need to find the MAC address or name of your Muse headset. 
 
-You will also need to find the MAC address of your Muse headset. **This code is
-only compatible with the 2016 version of the Muse headset.**
-
-Finally, the code for streaming and recording data is compatible with Python
-2.7 and Python 3.x. However, the code for stimulus presentation relies on
-psychopy and therefore only runs with Python 2.7.
+**This code is only compatible with the 2016 version of the Muse headset.**
 
 ## Usage
 
+*Everything can be run using muse-lsl.py or you may integrate into other packages.
+
 To stream data with LSL:
 
-`python muse-lsl.py`
+`python muse-lsl.py stream`
 
 The script will auto detect and connect to the first Muse device. In case you want
-a specific device or if the detection fails, find the name of the device and pass it to the script :
+a specific device or if the detection fails, find the name of the device and pass it to the script:
 
-`python muse-lsl.py --name YOUR_DEVICE_NAME`
+`python muse-lsl.py stream --name YOUR_DEVICE_NAME`
 
 You can also directly pass the MAC address (this option is also faster at startup):
 
-`python muse-lsl.py --address YOUR_DEVICE_ADDRESS`
-
-Once the stream is up and running, you can visualize it with
-
-`python lsl-viewer.py`
-
-## Available experimental paradigms
-
-The following paradigms are available:
-
-Paradigm | Stimulus presentation | Data | Analysis
----------|-----------------------|------|---------
-Visual P300 | `stimulus_presentation/generate_Visual_P300.py` `stimulus_presentation/generate_Visual_P300_stripes.py`| `data/visual/P300/` | [click here](https://github.com/alexandrebarachant/muse-lsl/blob/master/notebooks/P300%20with%20Muse.ipynb)
-Auditory P300 | `stimulus_presentation/generate_Auditory_P300.py` | `data/auditory/P300` | [click here](https://github.com/alexandrebarachant/muse-lsl/blob/master/notebooks/Auditory%20P300%20with%20Muse.ipynb)
-N170 | `stimulus_presentation/generate_N170.py` | `data/visual/N170` | [click here](https://github.com/alexandrebarachant/muse-lsl/blob/master/notebooks/N170%20with%20Muse.ipynb)
-SSVEP | `stimulus_presentation/generate_SSVEP.py` | `data/visual/SSVEP` | [click here](https://github.com/alexandrebarachant/muse-lsl/blob/master/notebooks/SSVEP%20with%20Muse.ipynb)
-SSAEP | `stimulus_presentation/generate_SSAEP.py` | `data/auditory/SSAEP` | [click here](https://github.com/alexandrebarachant/muse-lsl/blob/master/notebooks/SSAEP%20with%20Muse.ipynb)
-Spatial frequency | `stimulus_presentation/generate_spatial_gratings.py` | `data/visual/spatial_freq` | [click here](https://github.com/alexandrebarachant/muse-lsl/blob/master/notebooks/Spatial%20Frequency%20Task%20with%20Muse.ipynb)
-
-The stimulus presentation scripts can be found under `stimulus_presentation/`.
-Some pre-recorded data is provided under `data/`, alongside analysis notebooks under `notebooks`.
-
-### Visual P300
-
-The task is to count the number of cat images that you see. You can add new jpg images inside the [stimulus_presentation](stimulus_presentation/) directory: use the `target-` prefix for cat images, and `nontarget-` for dog images.
-
-### Auditory P300
-
-The task is to count the number of high tones that you hear.
-
-### N170
-
-The task is to mentally note whether a "face" or a "house" was just presented.
-
-### SSVEP
-
-The task is to passively fixate the center of the screen.
-
-### SSAEP
-
-The task is to passively fixate the center of the screen while listening to the sounds you hear.
-
-### Spatial frequency gratings
-
-The task is to passively fixate the center of the screen.
-
-## Running an experiment
-
-First, you have to run the muse-lsl script as described above.
-
-In another terminal, run
+`python muse-lsl.py stream --address YOUR_DEVICE_ADDRESS`
 
-`python stimulus_presentation/PARADIGM.py -d 120 & python lsl-record.py -d 120`
+Once the stream is up and running, from another prompt, you can visualize it with:
 
-where `PARADIGM.py` is one of the stimulus presentation scripts described above (e.g., `generate_Visual_P300.py`).
+`python muse-lsl.py lslview`
 
-This will launch the selected paradigm and record data for 2 minutes.
+### Backends
+You can choose between gatt, bgapi, and bluemuse backends.
 
-For data analysis, check out [these notebooks](https://github.com/alexandrebarachant/muse-lsl/blob/master/notebooks/).
+* gatt - used on unix systems, interfaces with native Bluetooth stack.
+* bgapi - used with BLED112 dongle.
+* bluemuse - used on Windows 10, native Bluetooth stack, requires [BlueMuse](https://github.com/kowalej/BlueMuse/tree/master/Dist) installation. 
 
 ## Common issues
 
@@ -102,3 +50,9 @@ For data analysis, check out [these notebooks](https://github.com/alexandrebarac
 
 2. `pygatt.exceptions.BLEError: No characteristic found matching 273e0003-4c4d-454d-96be-f03bac821358` (Linux)
  - There is a problem with the most recent version of pygatt. Work around this by downgrading to 3.1.1: `pip install pygatt==3.1.1`
+ 
+ 
+3. Connection issues with BLED112 dongle (Windows):
+ - You may need to use the --interface argument to provide the appropriate COM port value for the BLED112 device. The default value is COM9. To setup or view the device's COM port go to:
+ `Control Panel\Hardware and Sound\Devices and Printers > Right Click > Bluetooth settings > COM Ports > (Add > Incoming)`
+ 
diff --git a/setup.py b/setup.py
index 21f2665..ef713e8 100644
--- a/setup.py
+++ b/setup.py
@@ -1,4 +1,16 @@
 from setuptools import setup, find_packages
+from shutil import copyfile
+import os
+
+def copy_docs():
+    docs_dir = 'muselsl/docs'
+    if not os.path.exists(docs_dir):
+        os.makedirs(docs_dir)
+
+    copyfile('README.md', docs_dir + '/README.md')
+    copyfile('blinks.png', docs_dir + '/blinks.png')
+
+copy_docs()
 
 setup(name='muse-lsl',
       version='1.0.0',
@@ -10,7 +22,7 @@
       license='BSD (3-clause)',
       scripts=['muselsl/muse-lsl.py'],
       packages=find_packages(),
-      package_data={'muselsl': ['README.md', 'LICENSE.txt']},
+      package_data={'muselsl': ['docs/blinks.png', 'docs/README.md']},
       include_package_data=True,
       zip_safe=False,
       install_requires=['bitstring', 'pylsl', 'pygatt', 'pandas', 'scikit-learn', 'numpy', 'seaborn', 'pexpect'],