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
	Respiration (RIP) Sensor Science Hour

	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 - Event Related Potentials (ERP) Detection
	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]
	Activity Distinction using Android Sensors
	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

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☌ Project Presentation ☌ GitHub Repository ☌ How to install biosignalsnotebooks Python package ? ☌ Signal Library

☌ Notebook Categories ☌ Notebooks by Difficulty ☌ Notebooks by Signal Type ☌ Notebooks by Tag

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

from biosignalsnotebooks.__notebook_support__ import css_style_apply
css_style_apply()

.................... CSS Style Applied to Jupyter Notebook .........................