biosignalsnotebooks | project logo [main files] Notebooks Grouped by Difficulty

Each Notebook content is summarized in his header through a quantitative scale ("Difficulty" between 1 and 5 stars) and some keywords (Group of "tags").

Grouping Notebooks by difficulty level, by signal type to which it applies or by tags is an extremelly important task, in order to ensure that the biosignalsnotebooks user could navigate efficiently in this learning environment.

Download, Install and Execute Anaconda
Download, Install and Execute Jupyter Notebook Environment
Pairing a Device at Windows 10 [biosignalsplux]
Signal Acquisition [OpenSignals]
Resolution - The difference between smooth and abrupt variations
Problems of low sampling rate (aliasing)
Store Files after Acquisition [OpenSignals]
EEG - Loading Data from PhysioNet
Load Signals after Acquisition [OpenSignals]
Load acquired data from .txt file
Digital Filtering - Using filtfilt
Signal to Noise Ratio Determination
Synchrony - Accelerometer Signal
Synchrony - Light Signal
Synchrony - Acoustic Signal
ACC Sensor - Unit Conversion
BVP Sensor - Unit Conversion
ECG Sensor - Unit Conversion
EDA Sensor - Unit Conversion
EEG Sensor - Unit Conversion
EMG Sensor - Unit Conversion
fNIRS Sensor - Unit Conversion
Goniometer Sensor - Unit Conversion
PZT Sensor - Unit Conversion
RIP Sensor - Unit Conversion
SpO2 Sensor - Unit Conversion
Rock, Paper or Scissor Game - Train and Classify [Volume 1]
Introduction to Android sensors
Quick-Start Guide
EEG - Electrode Placement
Load acquired data from .h5 file
Signal Loading - Working with File Header
Plotting of Acquired Data using Bokeh
Digital Filtering - A Fundamental Pre-Processing Step
Digital Filtering - EEG
Generation of a time axis (conversion of samples into seconds)
Generation of Poincaré Plot from ECG Analysis
Computing SNR for ECG Signals
Computing SNR for Slow Signals
Generation of Tachogram from ECG
EEG - Alpha Band Extraction
EMG Analysis - Time and Frequency Parameters
GON - Angular velocity estimation
ECG Analysis - Heart Rate Variability Parameters
Parameter Extraction - Temporal and Statistical Parameters
Rock, Paper or Scissor Game - Train and Classify [Volume 5]
Synchronising data from multiple Android sensor files into one file
EMG - Overview
Device Synchronisation - Cable, Light and Sound Approaches
Event Detection - Muscular Activations (EMG)
Force Platform - Center of Pressure Estimation
Rock, Paper or Scissor Game - Train and Classify [Orange]
Stone, Paper or Scissor Game - Train and Classify [Volume 3]
Rock, Paper or Scissor Game - Train and Classify [Volume 4]
Synchronising Android and PLUX sensors
Resampling of signals recorded with Android sensors
Fatigue Evaluation - Evolution of Median Power Frequency
Detection of Outliers
Event Detection - R Peaks (ECG)
Calculate Time of Flight
Signal Classifier - Distinguish between EMG and ECG
Rock, Paper or Scissor Game - Train and Classify [Volume 2]
Train a model for detecting the fist activity using Naive Bayes
BVP Signal Analysis - A Complete Tour
EDA Signal Analysis - A Complete Tour

Auxiliary Code Segment (should not be replicated by the user)

In [1]:
from biosignalsnotebooks.__notebook_support__ import css_style_apply
.................... CSS Style Applied to Jupyter Notebook .........................